diff --git a/EX1&2Beam.png b/EX1&2Beam.png new file mode 100644 index 00000000..8b4fff2f Binary files /dev/null and b/EX1&2Beam.png differ diff --git a/EX1_part1.ipynb b/EX1_part1.ipynb new file mode 100644 index 00000000..048d2977 --- /dev/null +++ b/EX1_part1.ipynb @@ -0,0 +1,498 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantaneous and delayed deflections on a concrete and composite beam - Obtaining camber of the beam\n", + "\n", + "A concrete beam with a span of L=6.75 m is considered, simply supported and subjected to the following uniformly distributed loads:\n", + "\n", + "- self weight: $q_{pp}$=12.2 kN/m\n", + "- Dead load: $q_{cm}$=5.2 kN/m\n", + "- Live load: $q_{sc}$=11.7 kN/m\n", + "\n", + "It is considered that 30% of the live load is quasi-permanent ($ψ_2$=0.3)\n", + "\n", + "![beam](EX1&2Beam.png)\n", + "\n", + "The section, 0.35 m wide and 0.50 m heigth, is reinforced with 6Φ20 on the lower face and 3Φ12 on the upper face. For calculation purposes, the cover is 5 cm. The concrete is class C25/30.\n", + "\n", + "The load history is as follows:\n", + "- After 14 days, a load equal to the characteristic load is removed and acts on this element.\n", + "- After a short time, 70% of the overload is removed, leaving the quasi-permanent charge that is supposed to be maintained indefinitely over time.\n", + "\n", + "As rheological parameters, φ(t,14)=2.5 and εc=0.4 mm/m can be adopted.\n", + "\n", + "It is requested:\n", + "1. Determine the total deflection at infinite time due to the quasi-permanent load. To do this, the method of EN 1992-1-1 7.4.3 will be applied and a discretization of the span into 10 parts will be considered.\n", + "2. Compare with the results if the equivalent stiffness of the center of the span is considered for the entire span." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from structuralcodes.codes.ec2_2004 import __materials__\n", + "from structuralcodes.materials.concrete import ConcreteEC2_2004,ConcreteEC2_2023\n", + "from structuralcodes.materials.reinforcement import ReinforcementEC2_2023, ReinforcementEC2_2004\n", + "from shapely import Polygon\n", + "from structuralcodes.geometry import SurfaceGeometry,CompoundGeometry\n", + "from structuralcodes.sections._reinforcement import (\n", + " add_reinforcement,\n", + " add_reinforcement_line,\n", + ")\n", + "from structuralcodes.sections._generic import GenericSection\n", + "from structuralcodes.materials.constitutive_laws import Elastic, ElasticPlastic,ParabolaRectangle,UserDefined\n", + "from structuralcodes.plots.section_plots import draw_section\n", + "import math\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "long_beam = 6.75\n", + "\n", + "# caracteriscic load\n", + "qk=12.2 + 5.2 + 11.7 # kN/m\n", + "# cuasipermant load\n", + "q_cuasip=12.2 + 5.2 + 11.7 * 0.3 # kN/m\n", + "\n", + "eps_cs = 0.4\n", + "fi = 2.5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Long-term mechanical properties of the uncracked section:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ecm = 31476 MPa\n", + "Ec_eff = 8993 MPa\n", + "Ec = 16667 MPa\n", + "Reinf. area = 495 cm2\n", + "Sect. area = 1750 cm2\n", + "I11_1 = 541390 cm4\n", + "cz_1 = 28.06 cm\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Materials\n", + "concrete = ConcreteEC2_2004(25)\n", + "Ec_eff=concrete.Ecm/(1+fi) # DGM: cannot we implement this in a formula in the concrete or the Eurocode?\n", + "Ec = concrete.constitutive_law.get_tangent(0)[0] # DGM: we should clarify the return type in the abstract class (all implementations return ArrayLike) # DGM: if scalar -> return scalar, if vector -> return vector\n", + "print(f\"Ecm = {round(concrete.Ecm)} MPa\")\n", + "print(f\"Ec_eff = {round(Ec_eff)} MPa\")\n", + "print(f\"Ec = {round(Ec)} MPa\")\n", + "\n", + "# The package does not compute the Inertia of homogeneus cross secction, so a \"concrete reinforcement\" with reinforcement areas multiplied by Es/Ec is used.\n", + "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=Ec, density=7850, ftk=550, epsuk=0.07) \n", + "n=200000/Ec_eff\n", + "\n", + "# Create section\n", + "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500))) # DGM: here it would be useful to create a wrapper function for creating rectangular cross-sections\n", + "geo = SurfaceGeometry(poly, concrete)\n", + "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12*math.sqrt(n), reinforcemnet, n=3) # DGM: why 12 * sqrt(2E5/Ec_eff)\n", + "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcemnet, n=6)\n", + "sec = GenericSection(geo)\n", + "gp = sec.gross_properties\n", + "i11_1 = gp.i11\n", + "cz_1=gp.cz\n", + "print(f\"Reinf. area = {round(gp.area_reinforcement/(10**2))} cm2\")\n", + "print(f\"Sect. area = {round(gp.area/(10**2))} cm2\")\n", + "print(f\"I11_1 = {round(i11_1/(10**4))} cm4\") \n", + "print(f\"cz_1 = {round(cz_1/(10),2)} cm\") \n", + "\n", + "\n", + "draw_section(sec,\"gross 'homogeneus' section\",math.sqrt(n))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['mc2010', 'ec2_2004', 'ec2_2023']\n", + "fck=25 MPa\n", + "fcd=17 MPa\n", + "Ecm=16667 MPa\n" + ] + } + ], + "source": [ + "# DGM: just for the sake of the example, I will show here how we can instantiate concrete classes setting a global design code\n", + "import structuralcodes.codes as codes\n", + "import structuralcodes.materials as materials\n", + "\n", + "print(codes.get_design_codes())\n", + "codes.set_design_code(design_code='ec2_2004') \n", + "# codes.set_design_code(design_code='ec2_2080') # DGM: it would be nice to raise error or print warning if the passed design code does not exist\n", + "\n", + "sample_concrete = materials.concrete.create_concrete(fck=25)\n", + "print(f\"fck={sample_concrete.fck} MPa\")\n", + "sample_fcd = sample_concrete.fcd()\n", + "print(f\"fcd={round(sample_fcd)} MPa\")\n", + "sample_Ecm = sample_concrete.constitutive_law.get_tangent(0)[0]\n", + "print(f\"Ecm={round(sample_Ecm)} MPa\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mechanical properties of the cracked section at long term:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "eps_z0 = -2.19e-05\n", + "x (mm) from z=0 = 219.4\n", + "I11_2 = 367781 cm4\n", + "cz_2 = 21.91 cm\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the cracked cross section\n", + "curv = 1e-7 # Small curvature which produces 'elastic' stresses in the most compressed fibre of the concrete section.\n", + "eps = sec.section_calculator.find_equilibrium_fixed_curvature(sec.geometry,0,curv,0)[0]\n", + "x=-eps/curv # Distance from the bottom to the neutral fibre\n", + "print(\"eps_z0 = \",round(eps,7))\n", + "print(\"x (mm) from z=0 = \",round(x,1))\n", + "\n", + "# 2) Create the cracked section (sec2)\n", + "poly2 = Polygon(((0, x), (350, x), (350, 0), (0, 0)))\n", + "geo2 = SurfaceGeometry(poly2, concrete)\n", + "geo2 = add_reinforcement_line(geo2, (50, 50), (300, 50), 12*math.sqrt(n), reinforcemnet, n=3)\n", + "geo2 = add_reinforcement_line(geo2, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcemnet, n=6)\n", + "sec2 = GenericSection(geo2)\n", + "i11_2 = sec2.gross_properties.i11\n", + "cz_2 = sec2.gross_properties.cz\n", + "print(f\"I11_2 = {round(i11_2/(10**4))} cm4\") \n", + "print(f\"cz_2 = {round(cz_2/(10),2)} cm\") \n", + "draw_section(sec2,\"cracked section\",math.sqrt(n))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the cracking moment:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "z neutral axis = 265.78\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "z neutral axis = 260.43\n", + "m_cracking (mkN) = 46.29\n", + "fctm (MPa) = 2.56\n" + ] + } + ], + "source": [ + "concrete = ConcreteEC2_2004(25)\n", + "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07) \n", + "\n", + "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n", + "geo = SurfaceGeometry(poly, concrete)\n", + "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12, reinforcemnet, n=3)\n", + "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n", + "sec = GenericSection(geo)\n", + "\n", + "from SLS_section_response import calculate_cracking_moment\n", + "# A concrete material failing at fctm is used for the cracking moment calculation.\n", + "m_cracking,_ = calculate_cracking_moment(sec,plot=True) # DGM: in the near future, i would implement plotting functions in another method/module/package,.,....\n", + "m_cracking = m_cracking/1e6 #mkN\n", + "print('m_cracking (mkN) = ', round(m_cracking,2))\n", + "print('fctm (MPa) = ',round(concrete.fctm,2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Checking deflections by calculation according to EN 1992 1-1 section 7.4.3\n", + "\n", + "Expresion 7.18:\n", + "$$\\alpha = \\zeta\\alpha_{II} + (1-\\zeta)\\alpha_{I}$$\n", + "$$(1/r) = \\zeta(1/r)_{II} + (1-\\zeta)(1/r)_{I}$$\n", + "where $(1/r)_{II}$ is the curvature of the fully cracked seciont and $(1/r)_{I}$ is the curvature of the uncracked section \n", + "Expresion 7.19:\n", + "$$\n", + "\\zeta = 1 - \\beta(M_{cr}/M) ^2 \n", + "$$\n", + "where $\\beta=0.5$ for sustained loads \n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Maximum deflection [mm] = 16.89\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\dgm\\AppData\\Local\\Temp\\ipykernel_41380\\3658154747.py:13: RuntimeWarning: divide by zero encountered in divide\n", + " zeta = 1-0.5*(m_cracking/m_x)**2\n" + ] + } + ], + "source": [ + "# DGM: I guess that this should be a part of a further development (like a BeamElement/LineElement class) if so...\n", + "\n", + "def bending_momment_x_beam(q,L,n_sections=11): # n_sections -> number of sections in the beam\n", + " \"\"\"Return M for a simply supported beam under a uniform load q.\"\"\"\n", + " x = np.linspace(0,L,n_sections)\n", + " # Maximum deflection at the centre of the span. Only the first half of the sections are required\n", + " x = x[:math.ceil(n_sections/2)] \n", + " m_x=q*x/2*(L-x)\n", + " return x,m_x\n", + "\n", + "def compute_zeta_expresion_7_19(m_cracking,m_x): # DGM: is this an standard expression?\n", + " \"\"\"Expresion 7.19 EN 1992 1-1.\"\"\"\n", + " zeta = 1-0.5*(m_cracking/m_x)**2\n", + " zeta[m_x == 0] = 0\n", + " return zeta\n", + "\n", + "def compute_curvature(m_x,E,I):\n", + " \"\"\"Return the curvature in n sections given the moments in each section.\"\"\"\n", + " return m_x/(E*1000*I/1000**4)\n", + "\n", + "x_beam,m_x_characteristic = bending_momment_x_beam(qk,long_beam)\n", + "x_beam,m_x_cuaisp = bending_momment_x_beam(q_cuasip,long_beam)\n", + "\n", + "zeta =compute_zeta_expresion_7_19(m_cracking,m_x_characteristic)\n", + "curv_1 = compute_curvature(m_x_cuaisp,Ec_eff,i11_1)\n", + "curv_2 = compute_curvature(m_x_cuaisp,Ec_eff,i11_2)\n", + "\n", + "# Expresion 7.18\n", + "curv = zeta*curv_2 + (1-zeta)*curv_1\n", + "\n", + "# curvature integration\n", + "deflection = np.trapz(curv*x_beam*1000,x_beam)\n", + "\n", + "\n", + "print('Maximum deflection [mm] =', round(deflection,2))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Shrinkage deflections :\n", + "\n", + "Expresion 7.21: \n", + "$\n", + "1/r_{cs} = \\epsilon_{cs}\\alpha_e S/I \n", + "$" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "S_I (mm3)= 241009\n", + "S_II (mm3)= 377817\n", + "I_I (mm4)= 5413898904\n", + "I_II (mm4)= 3677810527\n", + "Shrinkage deflection [mm] = 4.9\n", + "Total deflection [mm] = 21.8\n" + ] + } + ], + "source": [ + "a_fi20 = math.pi * 20**2/4 # DGM: wouldnt it be useful to obtain reinf. areas from the generic section?\n", + "a_fi12 = math.pi * 12**2/4\n", + "s_1 = (6*a_fi20 * (450-cz_1) + 3*a_fi12 * (50-cz_1)) # DGM: wouldnt it be useful to obtain S_1/S_2 from the generic section?\n", + "s_2 = (6*a_fi20 * (450-cz_2) + 3*a_fi12 * (50-cz_2))\n", + "s_= zeta*s_2 + (1-zeta)*s_1\n", + "i_= zeta*i11_2 + (1-zeta)*i11_1\n", + "print('S_I (mm3)= ',round(s_1))\n", + "print('S_II (mm3)= ',round(s_2))\n", + "#print(\"S = \",s_)\n", + "print('I_I (mm4)= ',round(i11_1))\n", + "print('I_II (mm4)= ',round(i11_2))\n", + "#print(\"I = \",i_)\n", + "curv_cs = eps_cs * n * s_/i_\n", + "\n", + "# curvature integration\n", + "deflection_cs = np.trapz(curv_cs*x_beam*1000,x_beam)\n", + "print('Shrinkage deflection [mm] =', round(deflection_cs,2))\n", + "print('Total deflection [mm] =', round(deflection+deflection_cs,2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Compare with the results if the equivalent stiffness of the center of the span is considered for the entire span.\n", + "\n", + "$$\n", + "f = 5/384 qL^4/EI\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def_1 = 5/384*q_cuasip*long_beam**4 / (Ec_eff*1000) / (i11_1/1000**4) *1000\n", + "def_2 = 5/384*q_cuasip*long_beam**4 / (Ec_eff*1000) / (i11_2/1000**4) *1000\n", + "zeta_ = 1-0.5*(m_cracking/(qk*long_beam**2/8))**2\n", + "def_ = zeta_*def_2 + (1-zeta_)*def_1\n", + "deflection_cs = eps_cs * n * s_[-1]/i_[-1] * long_beam**2 /8 *1000\n", + "\n", + "print ('zeta = ', round(zeta_,2))\n", + "print()\n", + "print ('CUSIPERMANTENT LOAD DEFLECTION:') \n", + "print ('deflection I (mm) = ', round(def_1,2))\n", + "print ('deflection II (mm) = ', round(def_2,2))\n", + "print ('deflection cuasip (mm) = ', round(def_,2))\n", + "print()\n", + "print ('SHRINKAGE DEFLECTION:') \n", + "print ('deflection_cs (mm) = ', round(deflection_cs,2))\n", + "print()\n", + "print ('TOTAL DEFLECTION:') \n", + "print ('deflection (mm) = ', round(def_ + deflection_cs,2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/EX2_part1.ipynb b/EX2_part1.ipynb new file mode 100644 index 00000000..961f664b --- /dev/null +++ b/EX2_part1.ipynb @@ -0,0 +1,281 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A concrete beam with a span of L=6.75 m is considered, simply supported and subjected to the following uniformly distributed loads:\n", + "- Self weight: $q_{sw}$=12.2 kN/m\n", + "- Dead load: $q_{dl}$=5.2 kN/m\n", + "- Live load: $q_{ll}$=11.7 kN/m\n", + "\n", + "It is considered that 30% of the live load is quasi-permanent (ψ2=0.3)\n", + "\n", + "![beam](EX1&2Beam.png)\n", + "\n", + "The section, 0.35 m wide and 0.50 m height, is reinforced with 6φ20 on the lower face and 3φ12 on the upper face. For calculation purposes, the cover is 5 cm. The concrete is class C25/30. The structure is located 2 km from the coast in\n", + "the open. A creep coefficient of 2.5 will be considered.\n", + "\n", + "It is requested:\n", + "1. Determine the characteristic crack opening in the critical section.\n", + "2. Evaluate the admissibility of the crack opening applying EN 1992-1-1." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "from structuralcodes.codes.ec2_2004 import __materials__\n", + "from structuralcodes.codes.ec2_2004 import _section_7_3_crack_control\n", + "from structuralcodes.materials.concrete import ConcreteEC2_2004,ConcreteEC2_2023\n", + "from structuralcodes.materials.reinforcement import ReinforcementEC2_2023, ReinforcementEC2_2004\n", + "from shapely import Polygon\n", + "from structuralcodes.geometry import SurfaceGeometry,CompoundGeometry\n", + "from structuralcodes.sections._reinforcement import (\n", + " add_reinforcement,\n", + " add_reinforcement_line,\n", + ")\n", + "from structuralcodes.sections._generic import GenericSection\n", + "from structuralcodes.materials.constitutive_laws import Elastic, ElasticPlastic,ParabolaRectangle,UserDefined\n", + "from structuralcodes.plots.section_plots import draw_section,get_stress_point,draw_section_response\n", + "from SLS_section_response import calculate_strain_profile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define first the quasipermanent moment and an instance of the beam section" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "long_beam = 6.75\n", + "\n", + "# caracteriscic load\n", + "qk=12.2 + 5.2 + 11.7 # kN/m\n", + "# cuasipermant load\n", + "q_cuasip=12.2 + 5.2 + 11.7 * 0.3 # kN/m\n", + "\n", + "fi = 2.5\n", + "\n", + "# Mcuasip at midspan\n", + "m_cuasip = q_cuasip * long_beam**2/8\n", + "\n", + "# Define the cross section\n", + "concrete = ConcreteEC2_2004(25)\n", + "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07) \n", + "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n", + "geo = SurfaceGeometry(poly, concrete)\n", + "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12, reinforcemnet, n=3)\n", + "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n", + "sec = GenericSection(geo)\n", + "draw_section(sec,'Cross section EX2')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1.) Calculation of $\\epsilon_{sm} - \\epsilon_{cm}$:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculation of $\\sigma_s$" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Ba9aswejRo/Gf//wHcrkcP//8M55//nm0adMGo0aNgqOjI86fP48zZ85g27ZtAID27dsDAN577z0EBwdDoVBg6NCh5cb45Zdfqu9x8u6770JPTw+//vor8vLy6uQeHgEBAXj77bcxffp0xMfHo2/fvtDX18elS5ewcuVKfP/993jppZewePFizJ8/Hy+++CKaNGmCzMxMLFiwACqVqkxj1bRpU3Tr1g1jxoxBXl4e5s6dC2tra3z00UfqMrNmzUJISAj8/f3xxhtvqIckNjc3L3Mvncr44osvsG/fPoSGhsLNzQ0pKSmYP38+nJ2d1b8mjhw5EitWrMA777yDPXv2oGvXrigqKsL58+exYsUKbNu2Tf2L9bO4ePEievfujSFDhsDb2xt6enpYs2YNkpOTNf4P2rdvj59//hlffvklmjZtCjs7O/XF3uVRqVT4+eefMXLkSLRr1w5Dhw6Fra0tbty4gU2bNqFr16748ccfK7X/qryfRFSz2DY9Hdsm3WmbAOC5557DkiVLYG5uDm9vb8TGxmLnzp2wtrZ+5rhJy9XpGJCk07Zs2SJef/114eXlJUxNTYVSqRRNmzYV48aNE8nJyWXK//XXX6Jbt27CxMREmJiYCC8vLxERESEuXLigUe7vv/8Wffr0EWZmZsLExET4+vqKefPmqZcXFhaKcePGCVtbWyGTyTSGJ8ZjQxILIcSxY8dEcHCwMDU1FcbGxqJnz54iJiZGo0zpMLWPDwlc3tCz5QkPDxcmJiYVLv/f//4n2rdvL4yMjISZmZnw8fERH330kUhMTFTHOGzYMOHq6ioMDAyEnZ2deO6558TRo0fV2ygdknjWrFni22+/FS4uLsLAwEB0795dnDhxosw+d+7cKbp27SqMjIyESqUSzz//vDh79qxGmdIhiR8farj09UhISBBCCLFr1y4xYMAA4eTkJJRKpXBychLDhg0TFy9e1FgvPz9ffPPNN6Jly5bCwMBAWFpaivbt24upU6eK9PT0J76GlXX37l0REREhvLy8hImJiTA3Nxd+fn5ixYoVGuWSkpJEaGioMDMzEwDUwxNX9F6X2rNnjwgODhbm5ubC0NBQNGnSRLz22mvq96Iy+6/M+0lEtYNt07/YNpXQ9bbp/v37YtSoUcLGxkaYmpqK4OBgcf78eeHm5ibCw8PV5SoaLr9ly5ZlthkeHi7c3NxqoupUi2RC8EpAIm107do1eHh4YNasWfjPf/4jdThERERsm4hqEa8xIyIiIiIikhgTMyIiIiIiIokxMSMiIiIiIpIYrzEjIiIiIiKSGM+YERERERERSYyJGRERERERkcR4g2kAxcXFSExMhJmZGWQymdThEBE1GEIIZGZmwsnJCXI5fyssxXaJiEg6UrVNTMwAJCYmwsXFReowiIgarJs3b8LZ2VnqMLQG2yUiIunVddvExAyAmZkZgJIXX6VSSRwNEVHDkZGRARcXF/VxmEqwXSIiko5UbRMTM0DdTUSlUrEBJCKSALvraWK7REQkvbpum9ihn4iIiIiISGJMzIiIiIiIiCTGxIyIiIiIiEhiTMyIiIiIiIgkxsSMiIiIiIhIYkzMiIiIiIiIJMbEjIiIiIiISGJMzIiIiIiIiCTGxIyIiIiIiEhiTMyIqF7JysrC2LFj4ezsDCMjI3h7e+OXX3554jpnzpzB4MGD4e7uDplMhrlz55YpM2XKFMhkMo2Hl5dXLdWCiIiIapoQAp9//jkcHR1hZGSEoKAgXLp06YnrPK39v3btWpnlpY+VK1dWKb56k5j99NNPcHd3h6GhIfz8/HD48GGpQyIiCUyYMAFbt25FZGQkzp07h/Hjx2Ps2LFYv359hevk5OSgcePGmDFjBhwcHCos17JlS9y+fVv9+Pvvv2ujClSPsG0iItIeM2fOxA8//IBffvkFhw4dgomJCYKDg5Gbm/vE9Z7U/ru4uGgsu337NqZOnQpTU1OEhIRUKb56kZj9+eefmDBhAiZPnoxjx46hdevWCA4ORkpKitShEVEdi4mJQXh4OAIDA+Hu7o7Ro0ejdevWT/xC3LFjR8yaNQtDhw6FgYFBheX09PTg4OCgftjY2NRGFaieYNtERKQ9hBCYO3cuPv30UwwYMAC+vr74448/kJiYiLVr1z5x3Se1/wqFQmOZg4MD1qxZgyFDhsDU1LRKMdaLxGzOnDl46623MGrUKHW3JWNjY/z+++9Sh0ZEdaxLly5Yv349bt26BSEE9uzZg4sXL6Jv377PvO1Lly7ByckJjRs3RlhYGG7cuFEDEVN9xbaJiEgaDx48KDMvISEBSUlJCAoKUs8zNzeHn58fYmNjn7i9qrT/cXFxiI+PxxtvvFHluHU+McvPz0dcXJzGiyyXyxEUFFThi5yXl4eMjAyNBxHpjs2bN+OXX37B2bNnyyybN28evL294ezsDKVSiX79+uGnn35Cjx49nmmffn5+WLRoEbZu3Yqff/4ZCQkJ6N69OzIzM59pu1Q/VbVtYrtERFQzkpOTMW/ePBw5ckRjflJSEgDA3t5eY769vb16WXmq2v7/9ttvaNGiBbp06VLl2PWqvIaWuXv3LoqKisp9kc+fP1/uOtOnT8fUqVPrIjwiqqKcVatQnJ7+xDIpd+4gOS8Pa9euRadOndTzt2zZgkOHDuHgwYNYv3493NzcsG/fPkRERMDJyUnjS3JVPdpP3NfXF35+fnBzc8OKFSuq9asY1W9VbZvYLhHVPVFUhOxFi6QOg2pQWmEholJS8KC4GAsWLEDPnj3VyzZt2lStbVal/X/w4AGWLVuGzz77rFr70vnErDomTZqECRMmqKczMjLg4uIiYUREVKro9m0U37v3xDKlHRT69euHIUOGqOc3atQIvXv3xpo1axAaGgqg5CAaHx+P2bNnP1Ni9jgLCws0b94cly9frrFtUsPFdolIAsXFKPrnH6mjoBqSDWDVw792dnYYM2YMPvroI/XyvLw8ACVn1BwdHdXzk5OT0aZNm0rv50nt/6pVq5CTk4NXX321WnXQ+cTMxsYGCoUCycnJGvOTk5MrHF3NwMDgiRf4E5F2y3v418bGBq6urur5GRkZKCgogFyu2UtboVCguLi4RmPIysrClStXMHLkyBrdLtUPVW2b2C4REVVfHoC1ANJRkjiNGDECZmZmsLOzU5cRQsDBwQG7du1SJ2IZGRk4dOgQxowZU+l9Pan9/+233/DCCy/A1ta2WvXQ+WvMlEol2rdvj127dqnnFRcXY9euXfD395cwMiKqLaWD2hoZGWnMV6lUCAgIwMSJExEdHY2EhAQsWrQIf/zxB1588UV1uVdffRWTJk1ST+fn5yM+Ph7x8fHIz8/HrVu3EB8fr/Fr2H/+8x/s3bsX165dQ0xMDF588UUoFAoMGzasVutKuoltExFR3SgEsAHAHQAmJibqpOxxMpkM48ePx5dffon169fj1KlTePXVV+Hk5ISBAweqy/Xu3Rv/+9//1NOVbf8vX76Mffv24c0336x2XXT+jBlQct+i8PBwdOjQAZ06dcLcuXORnZ2NUaNGSR0aEdWwYvx7xuzxxAwAoqKiMGnSJISFheHevXtwc3PDV199hXfeeUdd5saNGxpn1RITE9G2bVv19OzZszF79mwEBAQgOjoaAPDPP/9g2LBhSE1Nha2tLbp164aDBw9W+1cxqv/YNhER1a5iAFsA/IOSH8TCwsJgbW1dYfmPPvoI2dnZGD16NNLS0tCtWzds3boVhoaG6jJXrlxBamqqerqy7f/vv/8OZ2fnZxoFWiaEENVeW4v8+OOPmDVrFpKSktCmTRv88MMP8PPzq9S6GRkZMDc3R3p6OlQqVS1HSkRPkjlv3hOvMcsF8MvD559++ikUCkWdxEW1o74ff6vbNtX314VIG4iCAmR8/bXUYVA1CQC7AJxGySULYWFh8PDwqJFtS3UMrhdnzABg7NixGDt2rNRhEFEtK+3GqK+vz6SMtB7bJiKi2hGDkqRMJpNh8ODBNZaUSUnnrzEjooalouvLiIiIqGE4BqD0LmWhoaFo0aKFlOHUGCZmRKRTnnR9GREREdVv5wHse/i8V69eaN++vZTh1CgmZkSkU0rPmD16oS4RERHVfwkAtj987ufnh27dukkZTo1jYkZEOoVdGYmIiBqeRACbUDISo4+PD4KDgyGTySSOqmYxMSMincKujERERA1LKoB1KLlnWdOmTTFgwIB6l5QBTMyISMewKyMREVHDkQFgDUp+mHV2dsbLL79cb0dlZmJGRDqFXRmJiIgahhyUJGVZAGxtbTF8+HAolUqJo6o9TMyISKcwMSMiIqr/8lHSffE+AJVKhREjRtT7tp+JGRHpFF5jRkREVL8VAtgAIBkl7f3IkSOhUqkkjqr2MTEjIp3Ca8yIiIjqr2IA2wDcBKCvr4+wsDDY2NhIHFXdYGJGRDqFZ8yIiIjqJwEgGsAlAHK5HK+88goaNWokbVB1iIkZEekUXmNGRERUPx0EcPLh80GDBqFJkyZShlPnmJgRkc4ofPgAmJgRERHVJycAHHr4vH///mjZsqWU4UiCiRkR6YzSbowymQwGBgaSxkJEREQ14wKAPQ+fBwQEoGPHjlKGIxkmZkSkMx4d+EMmk0kaCxERET276ygZ7AMAOnTogICAACnDkRQTMyLSGby+jIiIqP5IArARJSMxtmzZEiEhIQ36h1cmZkSkM0q7MnKofCIiIt12D8BaAAUAGjdujIEDB0Iub9ipScOuPRHpFJ4xIyIi0n2ZANagpF13cnLCkCFDoKenJ3FU0mNiRkQ6g4kZERGRbnuAkqQsE4C1tTWGDx/OAb0eYmJGRDqDXRmJiIh0VwGAdSjpxmhmZoYRI0bAxMRE4qi0BxMzItIZPGNGRESkm4pQMtBHEkp+YB0xYgQsLCykDUrLMDEjIp3BxIyIiEj3CADbUTI0vr6+PoYPHw47OzuJo9I+vMqOiHRGaVfG06dPIzExETKZTOMBoMy86pThtiou87izZ8/C2NgY7u7uNfxuExFRfVAIYCWAZAByuRwvv/wyXFxcJI5KOzExIyKdoXj4NzExEYmJiZLG0lA9mqwVFxer50dERMDGxkajbFZWFj755BOsXbsWqamp8PDwwHvvvYd33nlHXeb9998HANjb28PU1BRdunTBN998Ay8vr3L3X1BQgE8//RSbN2/G1atXYW5ujqCgIMyYMQNOTk41XV0iInpGC/DvD6sDBgxAs2bN1MuEEJg8eTIWLFiAtLQ0dO3aFT///LNGmSeZMWMGJk2ahPfffx9z584FANy7dw+TJ0/G9u3bcePGDdja2mLgwIGYNm0azM3Na7ZyNYyJGRFpleJ79ypc1hOAK0puRCkezhOPPR6fVx/K1OZ2q0oIofG3VHp6epnEbMKECdi9ezciIyPh7u6O7du3491334WTkxNeeOEFAECbNm0AAIcPH0ZBQQGmTJmCvn37IiEhAQqFAo/LycnBsWPH8Nlnn6F169a4f/8+3n//fbzwwgs4evRoNWtFRA1RUVaW1CHUe7/j36TM1NQUvr6+GstnzpyJH374AYsXL4aHhwc+++wzBAcH4+zZs08d6OvIkSP49ddfy2yz9Mfb2bNnw9vbG9evX8c777yDxMRErFq1qgZrV/Nk4vHWtQHKyMiAubk50tPToVKppA6HqEFLnzpV6hAajOomeOsApDyynZEjR6Jx48Zltt+qVSu88sor+Oyzz9Tz2rdvj5CQEHz55ZcAyh5/T548idatW+Py5cto0qRJpepx5MgRdOrUCdevX4erq2ul1tF2bJeIal9haiqyf/xR6jDqrWX4t61QKpWYNGmSxnIhBJycnPDhhx/iP//5D4CSH/ns7e2xaNEiDB06tMJtZ2VloV27dpg/fz6+/PJLtGnTRn3GrDwrV67EiBEjkJ2dXan7pUl1DObgH0REDZTs4UOOkm6ieg8f+gCUAAwAGD58GAEwRsl1Ao8mZREREeUmZQDQpUsXrF+/Hrdu3YIQAnv27MHFixfRt2/fcstnZ2dj4cKF8PDwqNL1B+np6ZDJZBzdi4hISzz6A55cLsfEiRPLlElISEBSUhKCgoLU88zNzeHn54fY2Ngnbj8iIgKhoaEa6z5JaYKl7Tex1u7oiIhIKxQC+D/8OzKmXC7Hf/7znyeOkDlv3jyMHj0azs7O0NPTg1wux4IFC9CjR48yZZ2cnJCdnQ1PT0/s2LEDSqWyUnHl5ubi448/xrBhw3hmiYhIC+wCkPDwuUwmw8SJE8tNiJKSkgCUXGP8KHt7e/Wy8kRFReHYsWM4cuRIpeK5e/cupk2bhtGjR1eqvJR4xoyIiJ6oEMDP+Dcp09fXx6RJkzSSsqVLl8LU1FT92L9/P+bNm4eDBw9i/fr1iIuLw7fffouIiAjs3LmzzD7279+PvXv3onnz5hgyZAhyc3PLlHlcQUEBhgwZAiEEfv7555qpLBERVdthAKcemX7vvffU14o93k4UFBRUefs3b97E+++/j6VLlz71GjSgpEtiaGgovL29MWXKlCrvr67xjBkREVUoC8Bv+PdaMxMTE/W1AI964YUX4Ofnp55u1KgRevfujTVr1iA0NBQA4Ovri/j4eMyePbtM95MmTZpApVKhc+fOsLS0xJo1azBs2LAK4ypNyq5fv47du3fzbBkRkcTOAoh5ZPrNN9/U6GL+eDuRl1cyLEhycjIcHR3V85OTk9UDQz0uLi4OKSkpaNeunXpeUVER9u3bhx9//BF5eXnqgaMyMzPRr18/mJmZYc2aNdDX13/mOtY2JmZERFrsSYNyVGZZZQf4KG+dTABbHonFxsYGERER5cZpZmYGMzMz9XRGRgYKCgogl2t2zFAoFBrD7JeprxAQQqgb7PKUJmWXLl3Cnj17YG1tXWFZIiKqff+g5AbSpV5++WU0atRIo8zj7YQQAg4ODti1a5c6EcvIyMChQ4cwZsyYcvfTu3dvnDp1SmPeqFGj4OXlhY8//lidlGVkZCA4OBgGBgZYv359pc6uaQMmZkSkE/IBnADwANVPSGpjnapurzrltYG7uzvCw8MrXV6lUiEgIAATJ06EkZER3NzcsHfvXvzxxx+YM2cOAODq1atYvHgxgJLuKenp6ZgxYwaMjIzQv39/9ba8vLwwffp0vPjiiygoKMBLL72EY8eOYePGjSgqKlJfi2BlZVXpa9OIiKhm3APw6CD0wcHB8Pb2fup6MpkM48ePx5dffolmzZqph8t3cnLCwIED1eV69+6NF198EWPHjoWZmRlatWqlsR0TExNYW1ur52dkZKBv377IyclBZGQkMjIykJGRAQCwtbUt91Ys2oKJGRFpFz09oLCwzOwLAA7UfTQ6R/bIAyhp+MrMK2e+7JH5AJBRVKRODFu3bq3RSFZWVFQUJk2ahLCwMNy7dw9ubm746quv1DeYNjQ0VI+81bZtW9jb26NHjx6IiYmBnZ2dejsXLlxAeno6AODWrVtYv349AJTp6rJnzx4EBgZWOU4iaqB05CyKNitCybD4pTp37ozOnTtXev2PPvoI2dnZGD16NNLS0tCtWzds3bpV4wzXlStXcPfu3Upv89ixYzh06BAAoGnTphrLEhIS4O7uXult1TXexwy8XwyRNsmcN6/cm0zHoOSiYkdHRzRu3LgksajDB4A632d1YqgpWVlZmDdvHvr06YMOHTrU2HYfx+Nv+fi6ENU+UVCAjK+/ljoMnSVQ0n3x3MPpFi1aYMiQIRJGVHOkOgbzjBkR6YQHD/82a9YMPXv2lDSWhsDU1LTMzUCJiIhK/Y2SpEwmk+GVV16Bp6en1CHpPA6XT0Q6oXTwdGNjY0njICIiauiOAoh7+PyFF15gUlZDmJgRkU4oPWPGxIyIiEg6Z1BytgwA+vTpU+HQ9lR1TMyISCfwjBkREZG0rgDY+fB5ly5d0KVLFynDqXeYmBGRTuAZMyIiIun8A2AzSgb9aNOmDYKCgiSOqP5hYkZEWk+AZ8yIiIikcgfAepQMj+/p6Ynnn3++RkcCphJMzIhI6xWgpDEAACMjIylDISIialDSAawBkA/A1dUVgwcPhlzOFKI28FUlIq1X2o1RT08P+vr6ksZCRETUUGQDWA0gB4C9vT2GDRvGdrgWMTEjIq1X2o3RyMiIXSeIiIjqQB6AtSg5Y2ZhYYGwsDAYGhpKG1Q9x8SMiLQeB/4gIiKqO4UouabsDgATExOMHDkSZmZmEkdV/zExIyKtx4E/iIiI6kYxgC0AbgFQKpUICwuDlZWVxFE1DEzMiEjr8YwZERFR7RMAdqHkfmUKhQLDhg2Do6OjxFE1HEzMiEjrPXqNGREREdWOGABnAMhkMgwePBju7u4SR9SwMDEjIq3HM2ZERES16xiAIw+fP/fcc2jRooWU4TRITMyISOsxMSMiIqo95wDse/i8V69eaNeunZThNFhMzIhI63HwDyIiotqRAGDHw+d+fn7o1q2blOE0aEzMiEjr8YwZERFRzUsEsAklIzH6+voiODiY9wuVEBMzItJ6pYkZB/8gIiKqGXcBrEPJPcuaNm2KF154gUmZxJiYEZFWE2BXRiIiopqUAWANgDwAzs7OePnll6FQKCSOipiYEZFWKwBQ9PA5EzMiIqJnk4OSpCwbgK2tLYYPHw6lUilxVAQwMSMiLVfajVGhUEBfX1/SWIiIiHRZPoC1AO4DMDc3x4gRI3iZgBZhYkZEWu3Rbozs+05ERFQ9hQA2AEhBSZs6YsQIqFQqiaOiRzExIyKtxhEZiYiInk0xgG0AbgJQKpUICwuDjY2NxFHR45iYEZFW48AfRERE1ScA7AFwCYBcLscrr7wCJycniaOi8jAxIyKtxjNmRERE1XcQwKmHzwcNGoTGjRtLGQ49gaSJ2b59+/D888/DyckJMpkMa9eu1VguhMDnn38OR0dHGBkZISgoCJcuXdIoc+/ePYSFhUGlUsHCwgJvvPEGsrKy6rAWRFSbSs+Y8eJkqitsm4iovogHcOjh8/79+6Nly5YSRkNPI2lilp2djdatW+Onn34qd/nMmTPxww8/4JdffsGhQ4dgYmKC4OBg5ObmqsuEhYXhzJkz2LFjBzZu3Ih9+/Zh9OjRdVUFIqplPGNGdY1tExHVBxcARD98HhgYiI4dO0oYDVWGnpQ7DwkJQUhISLnLhBCYO3cuPv30UwwYMAAA8Mcff8De3h5r167F0KFDce7cOWzduhVHjhxBhw4dAADz5s1D//79MXv2bPafJaoHmJhRXWPbRES67jpKBvsAgI4dO6JHjx5ShkOVpLXXmCUkJCApKQlBQUHqeebm5vDz80NsbCwAIDY2FhYWFuqGDwCCgoIgl8tx6NChMtsslZeXh4yMDI0HEWkndmUkbVJbbRPbJSKqKbcBbETJSIwtW7ZESEgIbzejI7Q2MUtKSgIA2Nvba8y3t7dXL0tKSoKdnZ3Gcj09PVhZWanLlGf69OkwNzdXP1xcXGo4eiKqKTxjRtqkttomtktEVBPuAVgHoABAkyZN8OKLLzIp0yFam5jVpkmTJiE9PV39uHnzptQhEVEFOFw+NQRsl4joWWUCWIOSdrNRo0YYMmQIFAqFxFFRVWhtYubg4AAASE5O1pifnJysXubg4ICUlBSN5YWFhbh37566THkMDAygUqk0HkSkfQR4xoy0S221TWyXiOhZPEBJUpYJwNraGsOHD4dSqZQ4KqoqrU3MPDw84ODggF27dqnnZWRk4NChQ/D39wcA+Pv7Iy0tDXFxceoyu3fvRnFxMfz8/Oo8ZiKqWfkAih4+Z2JG2oBtExFpm3yUdF+8B0ClUmHkyJFsM3WUpKMyZmVl4fLly+rphIQExMfHw8rKCq6urhg/fjy+/PJLNGvWDB4eHvjss8/g5OSEgQMHAgBatGiBfv364a233sIvv/yCgoICjB07FkOHDuWoV0T1QOnZMn19ff7yR3WGbRMR6YoiAJsAJKFkkKwRI0bA3Nxc4qiouiRNzI4ePYqePXuqpydMmAAACA8Px6JFi/DRRx8hOzsbo0ePRlpaGrp164atW7fC0NBQvc7SpUsxduxY9O7dG3K5HIMHD8YPP/xQ53UhoprHbowkBbZNRKQLBIDtKBkaX19fH8OHD4etra3EUdGzkAkhhNRBSC0jIwPm5uZIT09nv34iiWXOm4fie/cAAFcAbADg5OSEt956S9K4qHbw+Fs+vi5EtU8UFCDj66+lDqNaBIC9AOIByOVyDBs2DE2bNpU2qHpEqmOw1l5jRkRUesbMxMRE0jiIiIi0yWGUJGUAMHDgQCZl9QQTMyLSWuzKSEREpOkkgNiHz/v16wcfHx8pw6EaxMSMiLRWzsO/PGNGREQEXAKw++Hz7t27c6TXeoaJGRFpLZ4xIyIiKnETwNaHz9u1a6cxSBHVD0zMiEhr8YwZERERkAxgPUqGx2/RogVCQ0Mhk8kkjopqGhMzItJaHPyDiIgauvsA1gIoAODu7o5BgwZBLudX+PqI7yoRaS12ZSQiooYsC8BqlLSHjo6OGDp0KPT0JL0NMdUiJmZEpJUEeMaMiIgarlwAawBkArCyskJYWBgMDAwkjopqExMzItJK+SjpSw/wjBkRETUsBSi5piwVgKmpKUaMGMEfKRsAJmZEpJVKz5bp6+tDqVRKGgsREVFdKQKwGUAiAENDQ4wYMQKWlpYSR0V1gYkZEWml0hEZebaMiIgaCgFgJ4AEAHp6ehg2bBjs7e0ljorqChMzItJKvL6MiIgaEgFgP4BzAGQyGV5++WW4urpKHBXVJSZmRKSVmJgREVFDchTAsYfPBwwYgObNm0sZDkmAiRkRaSV2ZSQioobiNIADD5/36dMHrVu3ljIckggTMyLSSjxjRkREDcFlALsePu/atSu6dOkiZTgkISZmRKSVeHNpIiKq7/4BsAUl15e1adMGvXv3ljgikhITMyLSSqVdGXnGjIiI6qM7KLlXWREAT09PPP/885DJZBJHRVJiYkZEWoldGYmIqL5KA7AGQD4ANzc3DB48GHI5v5Y3dPwPICKtxK6MRERUH2WjJCnLAWBvb4+hQ4dCX19f4qhIGzAxIyKtI8AzZkREVP/koSQpSwdgaWmJESNGwNDQUOKoSFswMSMirZOPkj73ABMzIiKqHwpRck3ZXZS0bSNGjICpqanEUZE2YWJGRFqn9GyZvr4+u3cQEZHOKwawGcAtAAYGBhgxYgSsrKwkjoq0DRMzItI6HJGRiIjqC4GS+5RdBaBQKDB06FA4ODhIHBVpIyZmRKR1OPAHERHVFwcAnAEgk8nw0ksvwd3dXeKISFsxMSMircOBP4iIqD44BuDow+fPPfccvLy8pAyHtBwTMyLSOuzKSEREuu4sgH0Pn/fu3Rvt2rWTMhzSAUzMiEjrsCsjERHpsgQAOx4+79y5M7p27SplOKQjmJgRkdZhYkZERLoqEcAmlAz64evri759+0Imk0kcFekCJmZEpHXYlZGIiHTRXQDrUHLPsmbNmuGFF15gUkaVxsSMiLQOB/8gIiJdkw5gDYA8AC4uLnj55ZehUCgkjop0CRMzItI6TMyIiEiX5KAkKcsGYGtri2HDhkFfX1/iqEjXMDEjIq0ihFB3ZeQ1ZkREpO3yAKwFkAbA3NwcI0aMgJGRkaQxkW5iYkZEWiVfCBQ/fM4zZkREpM0KAWwEkIKSHxNHjhwJlUolcVSkq5iYEZFWyRECAKBUKtkNhIiItFYxgG0AbqKkzQoLC4O1tbXEUZEuY2JGRFrlQXHJ+TJ2YyQiIm0lAOwBcAmAQqHAK6+8AicnJ4mjIl3HxIyItErpGTN2YyQiIm11EMCph88HDRqExo0bSxkO1RNMzIhIq+Q8PGPGxIyIiLRRPIBDD5+HhobC29tbwmioPmFiRkRa5cHDM2bsykhERNrmPIDoh88DAwPRoUMHCaOh+oaJGRFpFZ4xIyIibXQNwPaHzzt27IgePXpIGA3VR0zMiEir8BozIiLSNrdRMix+MYBWrVohJCQEMplM4qiovmFiRkRahaMyEhGRNrkHYB1K7lnWpEkTDBw4kEkZ1QomZkSkVXjGjIiItEUmgNUAcgE0atQIQ4YMgUKhkDgqqq+YmBGRVnnAa8yIiEgLPEBJUpYFwMbGBsOHD4dSqZQ4KqrPmJgRkdYQQqhHZWRiRkREUskHsBbAfQAqlQojRoxgF3uqdUzMiEhr5ObmovjhczaAREQkhSKUDPSRDMDIyAgjRoyAubm5xFFRQ8DEjIi0RnZ2NgDAwMAAenp6EkdDREQNjQCwDcANAPr6+hg+fDhsbW0ljooaCiZmRKQ1ShMzni0jIqK6JlBy8+iLAORyOYYMGQJnZ2dpg6IGhYkZEWmNnJwcALy+jIiI6t5hACcePh84cCCaNm0qZTjUADExIyKtUXrGjIkZERHVpZMAYh8+79evH3x8fKQMhxooJmZEpDXYlZGIiOraJQC7Hz7v0aMH/Pz8pAyHGjAmZkSkNXjGjIiI6tINAFsfPm/fvj0CAwMljIYaOiZmRKQ1eI0ZERHVlcTbt7EBJcPje3t7o3///pDJZFKHRQ0YEzMi0ho8Y0ZERHUhNTUVy6KiUADAw8MDL774IuRyfi0mafE/kIi0Bq8xIyKi2paRkYElS5Yg58EDODo64pVXXuG9M0krMDEjIq2Rm5sLADAyMpI4EiIiqo8ePHiAyMhIpKenQ2lmjrCwMBgYGEgdFhEAJmZEpIXYx5+IiGpaQUEBli9fjjt37kDPyBhNAvuz6zxpFSZmRERERFSvFRUVYeXKlbh58ybk+kp4BPSH0tRM6rCINDAxIyIiIqJ6SwiB9evX49KlS5ApFHDv0Q+GFlZSh0VUBhMzIiIiIqqXhBDYvn07Tp48CchkcO3aBya2DlKHRVQuSROz6dOno2PHjjAzM4OdnR0GDhyICxcuaJTJzc1FREQErK2tYWpqisGDByM5OVmjzI0bNxAaGgpjY2PY2dlh4sSJKCwsrMuqEBFRPcB2iah+OXDgAA4ePAgAcO4UAJWTq8QREVVM0sRs7969iIiIwMGDB7Fjxw4UFBSgb9++6iGzAeCDDz7Ahg0bsHLlSuzduxeJiYkYNGiQenlRURFCQ0ORn5+PmJgYLF68GIsWLcLnn38uRZWIiEiHsV0iqj+OHTuGXbt2AQAc23aGpUdziSMiejKZEEJIHUSpO3fuwM7ODnv37kWPHj2Qnp4OW1tbLFu2DC+99BIA4Pz582jRogViY2PRuXNnbNmyBc899xwSExNhb28PAPjll1/w8ccf486dO1AqlU/db0ZGBszNzZGeng6VSlWrdSSiis2ZMweZmZkYPXo0HB0dpQ6H6oC2H3/ZLhHppvPnz2PFihUQQsC2RRs4tO5UpoxcBgxszraGypLqGKxV15ilp6cDAKysSi7IjIuLQ0FBAYKCgtRlvLy84OrqitjYWABAbGwsfHx81I0fAAQHByMjIwNnzpwpdz95eXnIyMjQeBARET2O7RKR7rl27RpWrVoFIQQsPTxh79tR6pCIKkVrErPi4mKMHz8eXbt2RatWrQAASUlJUCqVsLCw0Chrb2+PpKQkdZlHG7/S5aXLyjN9+nSYm5urHy4uLjVcGyIi0nVsl4h0z+3btxEVFYWioiKoGrmjUcfuvDcm6QytScwiIiJw+vRpREVF1fq+Jk2ahPT0dPXj5s2btb5PIiLSLWyXiHTLvXv3sHTpUuTl5cHE1hEuXXpBJtear7pET6UndQAAMHbsWGzcuBH79u2Ds7Ozer6DgwPy8/ORlpam8etkcnIyHBwc1GUOHz6ssb3S0bFKyzzOwMAABgYGNVwLIiKqL9guEemWzMxMREZGIjs7G4YW1nDrHgy5Qiu+5hJVmqQ/IwghMHbsWKxZswa7d++Gh4eHxvL27dtDX19fPaIOAFy4cAE3btyAv78/AMDf3x+nTp1CSkqKusyOHTugUqng7e1dNxUhIqJ6ge0Ske7Jzc3F0qVLcf/+fShNzOAeEAJFJQbZIdI2kv6UEBERgWXLlmHdunUwMzNT9703NzeHkZERzM3N8cYbb2DChAmwsrKCSqXCuHHj4O/vj86dOwMA+vbtC29vb4wcORIzZ85EUlISPv30U0RERPDXRyIiqhK2S0S6paCgAFFRUUhOToaeoRHcA0Ohb2QsdVhE1SJpYvbzzz8DAAIDAzXmL1y4EK+99hoA4LvvvoNcLsfgwYORl5eH4OBgzJ8/X11WoVBg48aNGDNmDPz9/WFiYoLw8HB88cUXdVUNIiKqJ9guEemO4uJi/PXXX7h+/Trk+vpwDwiBgRlvL0G6S6vuYyYV3i+GSDvwPmYND4+/5ePrQvRkQgisX78e8fHxkMkV8AjsDxO7qrUbvI8ZVYT3MSMiIiIiqoRdu3YhPj4ekMng2qV3lZMyIm3ExIyIiIiIdEZMTAwOHDgAAGjUsTtUzu7SBkRUQ5iYEREREZFOOHHiBHbs2AEAcGjdCVaNvSSOiKjmMDEjIiIiIq138eJFrFu3DgBg4+kLG6/WEkdEVLOYmBERERGRVrtx4wZWrlwJIQQs3JvBoY0fZDKZ1GER1SgmZkRERESktZKTk7F8+XIUFhbCzMkVzp0CmJRRvcTEjIiIiIi00v379xEZGYnc3FwY29jDtUsQZHJ+faX6if/ZRERERKR1srOzERkZiaysLBiYW8K9Rz/I9fSkDouo1jAxIyIiIiKtkpeXh6VLl+LevXvQNzaFR2B/KJQGUodFVKuYmBERERGR1igsLERUVBRu374NhYEhPAL7Q9/IROqwiGodEzMiIiIi0grFxcVYvXo1rl27BrmePjwCQmCgspA6LKI6wcSMiIiIiCQnhMCmTZtw7tw5yORyuHXvCyMrW6nDIqozTMyIiIiISHJ79uzBsWPHAAAu/r1gat9I4oiI6hYTMyIiIiKS1KFDh7B//34AgFOHbjB3aSxxRER1j4kZEREREUnm1KlT2Lp1KwDA3qcDrJt6SxwRkTSYmBERERGRJC5fvoy1a9cCAKybt4Ktd1tpAyKSEBMzIiIiIqpz//zzD1asWIHi4mKYuzaBY1t/yGQyqcMikgwTMyIiIiKqU3fu3MGyZctQUFAAUwdnOPsFMimjBo+JGRERERHVmfT0dERGRuLBgwcwsraDW7c+kCsUUodFJDkmZkRERERUJ3JycrBkyRJkZGTAQGUB9x79INfTlzosIq3AxIyIiIiIal1+fj6WLl2K1NRU6BubwD2gP/QMDKUOi0hrMDEjIiIiolpVVFSEP//8E4mJiVAoDeAeGAqlianUYRFpFSZmRERERFRrhBBYs2YNrl69CrmeHtwDQmCospA6LCKtw8SMiIiIiGqFEAJbtmzBmTNnIJPL4dq1L4yt7aQOi0grMTEjIiIiolqxb98+HDlyBADg3LknzBydJY6ISHsxMSMiIiKiGnfkyBFER0cDABzbdYGFaxNpAyLSckzMiIiIiKhGnTlzBps3bwYA2LVsB5vmrSSOiEj7VSsx69WrF6ZOnVpm/v3799GrV69nDoqIiKgq2C4RaY+rV69i9erVAACrpi1g16q9xBER6Qa96qwUHR2NU6dO4fjx41i6dClMTEwAlNyfYu/evTUaIBER0dOwXSLSDrdu3UJUVBSKi4th7tIYTu26QiaTSR0WkU6odlfGnTt3IikpCZ07d8a1a9dqMCQiIqKqY7tEJK27d+9i2bJlKCgogIl9Izh37gmZnFfNEFVWtT8tjo6O2Lt3L3x8fNCxY0f1xZ1ERERSYLtEJJ2MjAxERkYiJycHRpY2cOvWB3KFQuqwiHRKtRKz0lPSBgYGWLZsGd5//33069cP8+fPr9HgiIiIKoPtEpF0Hjx4gMjISKSnp0NpZg73gBAo9JVSh0Wkc6p1jZkQQmP6008/RYsWLRAeHl4jQREREVUF2yUiaeTn52PZsmW4c+cO9IyM4RHYH3qGRlKHRaSTqpWYJSQkwNbWVmPe4MGD4eXlhaNHj9ZIYERERJXFdomo7hUVFWHlypX4559/oFAawCOwP5QmZlKHRaSzqpWYubm5lTu/ZcuWaNmy5TMFREREVFVsl4jqlhAC69evx+XLlyFTKODWIxiG5lZSh0Wk0zhUDhERERFVmhAC27Ztw8mTJwGZHK5d+8DExkHqsIh0HhMzIiIiIqq0v//+G4cOHQIAOPsFQOXkKnFERPUDEzMiIiIiqpRjx45h9+7dAADHtv6wdG8mcURE9QcTMyIiIiJ6qnPnzmHjxo0AANsWbWDj6SNxRET1CxMzIiIiInqia9eu4a+//oIQApaNvWDv21HqkIjqHSZmRERERFSh27dvY/ny5SgqKoLK2R2NOnRT39SdiGoOEzMiIiIiKte9e/ewdOlS5Ofnw8TWES7+vSCT8+sjUW3gJ4uIiIiIysjMzMSSJUuQnZ0NQwtruHUPhlxRrVvgElElMDEjIiIiIg25ublYunQp0tLSoDRVwT0wBAqlUuqwiOo1JmZEREREpFZQUIDly5cjOTkZeoZG8AjsD31DY6nDIqr3mJgREREREQCguLgYq1atwo0bNyDXV8I9sD+UpiqpwyJqEJiYERERERGEENiwYQMuXrwImUIB9+7BMLKwljosogaDiRkRERERYefOnYiPjwdkMrh26Q0TO0epQyJqUJiYERERETVwMTExiImJAQA06tgDqkbu0gZE1AAxMSMiIiJqwOLj47Fjxw4AgENrP1g19pQ4IqKGiYkZERERUQN14cIFrF+/HgBg4+UL2xatJY6IqOFiYkZERETUAN24cQOrVq2CEAIW7s3h0NpP6pCIGjQmZkREREQNTHJyMpYtW4bCwkKYObnCuVMPyGQyqcMiatCYmBERERE1IPfv30dkZCTy8vJgbOMA1y5BkMn5lZBIavwUEhERETUQWVlZiIyMRFZWFgzNreDeIxhyPT2pwyIiMDEjIiIiahDy8vKwdOlS3Lt3D/omZnAPDIFCaSB1WET0EBMzIiIionqusLAQUVFRSEpKgp6BETwC+0PfyETqsIjoEUzMiIiIiOqx4uJirF69GteuXYNcTx/uASEwMDOXOiwiegwTMyIiIqJ6SgiBTZs24dy5c5DJ5XDrHgwjKxupwyKickiamP3888/w9fWFSqWCSqWCv78/tmzZol6em5uLiIgIWFtbw9TUFIMHD0ZycrLGNm7cuIHQ0FAYGxvDzs4OEydORGFhYV1XhYiI6gG2S1Tf7NmzB8eOHQNkMrj494apvZPUIRFRBSRNzJydnTFjxgzExcXh6NGj6NWrFwYMGIAzZ84AAD744ANs2LABK1euxN69e5GYmIhBgwap1y8qKkJoaCjy8/MRExODxYsXY9GiRfj888+lqhIREekwtktUnxw8eBD79+8HADTq0A3mLh4SR0RETyITQgipg3iUlZUVZs2ahZdeegm2trZYtmwZXnrpJQDA+fPn0aJFC8TGxqJz587YsmULnnvuOSQmJsLe3h4A8Msvv+Djjz/GnTt3oFQqK7XPjIwMmJubIz09HSqVqtbqRkRPNmfOHGRmZmL06NFwdHSUOhyqA7pw/GW7RLro5MmTWLNmDQDA3qcj7Fq2lTgi7SOXAQObs62hsqQ6BmvNNWZFRUWIiopCdnY2/P39ERcXh4KCAgQFBanLeHl5wdXVFbGxsQCA2NhY+Pj4qBs/AAgODkZGRob6183y5OXlISMjQ+NBRET0KLZLpKsuXbqEdevWAQCsm7eCrXcbaQMiokqRPDE7deoUTE1NYWBggHfeeQdr1qyBt7c3kpKSoFQqYWFhoVHe3t4eSUlJAICkpCSNxq90eemyikyfPh3m5ubqh4uLS81WioiIdBbbJdJlN2/exIoVK1BcXAxzt6ZwbOsPmUwmdVhEVAmSJ2aenp6Ij4/HoUOHMGbMGISHh+Ps2bO1us9JkyYhPT1d/bh582at7o+IiHQH2yXSVSkpKVi2bBkKCwth6ugC504BTMqIdIie1AEolUo0bdoUANC+fXscOXIE33//PV555RXk5+cjLS1N49fJ5ORkODg4AAAcHBxw+PBhje2Vjo5VWqY8BgYGMDDgne6JiKgstkuki9LT0xEZGYnc3FwYWdvBrWsQ5AqF1GERURVIfsbsccXFxcjLy0P79u2hr6+PXbt2qZdduHABN27cgL+/PwDA398fp06dQkpKirrMjh07oFKp4O3tXeexExFR/cN2ibRddnY2lixZgszMTBioLOHeox/kevpSh0VEVSTpGbNJkyYhJCQErq6uyMzMxLJlyxAdHY1t27bB3Nwcb7zxBiZMmAArKyuoVCqMGzcO/v7+6Ny5MwCgb9++8Pb2xsiRIzFz5kwkJSXh008/RUREBH95JCKiKmO7RLomLy8Py5YtQ2pqKvSNTeARGAI9A0OpwyKiapA0MUtJScGrr76K27dvw9zcHL6+vti2bRv69OkDAPjuu+8gl8sxePBg5OXlITg4GPPnz1evr1AosHHjRowZMwb+/v4wMTFBeHg4vvjiC6mqREREOoztEumSwsJCrFixAomJiVAoDeAeGAp9Y1OpwyKiatK6+5hJgfeLIdIOvI9Zw8Pjb/n4utDTFBcXY/Xq1Thz5gzkenrw6PkcjK3tpA5Lp/A+ZlSRBn8fMyIiIiJ6OiEEtmzZgjNnzkAml8O1W18mZUT1ABMzIiIiIh2yd+9eHD16FADg3LknzBycJY6IiGoCEzMiIiIiHXHkyBHs3bsXAODUvissXJtIHBER1RQmZkREREQ64MyZM9i8eTMAwK5lO1g3aylxRERUk5iYEREREWm5K1euYPXq1QAAq6besGvVXuKIiKimMTEjIiIi0mK3bt3Cn3/+ieLiYpi7NIZTuy6QyWRSh0VENYyJGREREZGWunv3LpYuXYqCggKY2jeCc+eekMn59Y2oPuInm4iIiEgLZWRkIDIyEg8ePICRlS1cu/WBXKGQOiwiqiVMzIiIiIi0TE5ODiIjI5Geng4DM3O49+gHhb5S6rCIqBYxMSMiIiLSIvn5+Vi+fDnu3LkDPSMTuAf2h56hkdRhEVEtY2JGREREpCWKioqwcuVK/PPPP1AoDeARGAKliZnUYRFRHWBiRkRERKQFhBBYt24dLl++DLlCD249+sHQ3ErqsIiojjAxIyIiIpKYEALbtm3DqVOnAJkcrl2DYGJjL3VYRFSHmJgRERERSezvv//GoUOHAADOfgEwc3KVOCIiqmtMzIiIiIgkFBcXh927dwMAHNv6w9K9mcQREZEUmJgRERERSeTcuXPYtGkTAMDWuw1sPH0kjoiIpMLEjIiIiEgCCQkJ+OuvvyCEgGVjL9j7dJQ6JCKSEBMzIiIiojp2+/ZtREVFoaioCCpndzTq0A0ymUzqsIhIQkzMiIiIiOpQamoqIiMjkZ+fDxM7R7j494JMzq9kRA0djwJEREREdSQzMxORkZHIycmBoaU13LoHQ67QkzosItICTMyIiIiI6kBubi4iIyORlpYGpakK7gEhUOgrpQ6LiLQEEzMiIiKiWlZQUIDly5cjJSUFeoZG8AjsD31DY6nDIiItwsSMiIiIqBYVFRVh1apVuHHjBuT6SrgH9ofSVCV1WESkZZiYEREREdUSIQQ2bNiAixcvQqZQwL17MIwsrKUOi4i0EBMzIiIiolqyc+dOnDhxApDJ4NolCCZ2jlKHRERaiokZERERUS04cOAAYmJiAADOnQKgauQmcUREpM2YmBERERHVsOPHj2Pnzp0AAIc2frD0aC5xRESk7ZiYEREREdWgCxcuYMOGDQAAGy9f2Hq1ljgiItIFTMyIiIiIasj169exatUqCCFg6dEcDq39pA6JiHQEEzMiIiKiGpCUlITly5ejsLAQZo3c0KhjD8hkMqnDIiIdwcSMiIiI6Bndv38fS5cuRV5eHoxtHODq3xsyOb9mEVHl8YhBRERE9AyysrKwZMkSZGVlwdDcCu49giHX05M6LCLSMUzMiIiIiKopNzcXS5cuxf3796FvYgb3wP5QKA2kDouIdBATMyIiIqJqKCwsxJ9//omkpCToGRjBI7A/9I2MpQ6LiHQUEzMiIiKiKiouLsZff/2Fa9euQa6nD/fAEBiYmUsdFhHpMCZmRERERFUghMDGjRtx/vx5yORyuHUPhpGljdRhEZGOY2JGREREVAW7d+/G8ePHAZkMLv69YWrvJHVIRFQPMDEjIiIiqqSDBw/i77//BgA06tAN5i4eEkdERPUFEzMiIiKiSjh58iS2bdsGALD37QirJi0kjoiI6hMmZkRERERPcenSJaxbtw4AYN28FWxbtJE2ICKqd5iYERERET3BzZs3sWLFChQXF8PCrSkc2/pDJpNJHRYR1TNMzIiIiIgqkJKSgmXLlqGwsBCmji5w9gtkUkZEtYKJGREREVE50tLSEBkZidzcXBhb28OtaxBkcn51IqLawaMLERER0WOys7MRGRmJzMxMGKgs4dYjGHI9fanDIqJ6jIkZERER0SPy8vKwbNkypKamQt/YFB6BIdAzMJQ6LCKq55iYERERET1UWFiIP//8E4mJiVAYGMIjsD/0jU2lDouIGgAmZkREREQAiouLsWbNGiQkJECupw/3gBAYqCykDouIGggmZkRERNTgCSGwefNmnD17FjK5HG7d+sDYylbqsIioAWFiRkRERA3e3r17ERcXBwBw7twTpg7OEkdERA0NEzMiIiJq0A4fPoy9e/cCAJzad4OFaxOJIyKihoiJGRERETVYp0+fxpYtWwAAdq3aw7qZt8QREVFDxcSMiIiIGqQrV65gzZo1AACrZt6wa9lO4oiIqCFjYkZEREQNzq1bt/Dnn3+iuLgY5q6N4dSuK2QymdRhEVEDxsSMiIiIGpS7d+9i6dKlKCgogKl9Izj79WRSRkSSY2JGREREDUZGRgaWLFmCBw8ewMjKFq7d+kKuUEgdFhEREzMiIiJqGHJycrBkyRJkZGTAwMwc7gEhUOjrSx0WEREAJmZERETUAOTn52PZsmW4e/cu9IxM4B4YCj0DQ6nDIiJSY2JGRERE9VpRURFWrFiBW7duQaE0gEdgfyhNTKUOi4hIAxMzIiIiqreEEFi3bh2uXLkCmUIP7j36wdDcUuqwiIjK0JrEbMaMGZDJZBg/frx6Xm5uLiIiImBtbQ1TU1MMHjwYycnJGuvduHEDoaGhMDY2hp2dHSZOnIjCwsI6jp6IiOojtk26TQiBrVu34tSpU4BMDrdufWBsYy91WERE5dKKxOzIkSP49ddf4evrqzH/gw8+wIYNG7By5Urs3bsXiYmJGDRokHp5UVERQkNDkZ+fj5iYGCxevBiLFi3C559/XtdVICKieoZtk+7bv38/Dh8+DABw8QuEmaOLxBEREVVM8sQsKysLYWFhWLBgASwt/+1akJ6ejt9++w1z5sxBr1690L59eyxcuBAxMTE4ePAgAGD79u04e/YsIiMj0aZNG4SEhGDatGn46aefkJ+fL1WViIhIx7Ft0n1xcXHYs2cPAMCxXRdYuDeVOCIioieTPDGLiIhAaGgogoKCNObHxcWhoKBAY76XlxdcXV0RGxsLAIiNjYWPjw/s7f/tlhAcHIyMjAycOXOmwn3m5eUhIyND40FERFSqrtsmtks16+zZs9i0aRMAwNa7LWyat5I4IiKip9OTcudRUVE4duwYjhw5UmZZUlISlEolLCwsNObb29sjKSlJXebRhq90eemyikyfPh1Tp059xuiJiKg+kqJtYrtUcxISErB69WoIIWDVxAv2Ph2kDomIqFIkO2N28+ZNvP/++1i6dCkMDev2PiKTJk1Cenq6+nHz5s063T8REWknqdomtks1IzExEVFRUSgqKoLK2R1O7btBJpNJHRYRUaVIlpjFxcUhJSUF7dq1g56eHvT09LB371788MMP0NPTg729PfLz85GWlqaxXnJyMhwcHAAADg4OZUbCKp0uLVMeAwMDqFQqjQcREZFUbRPbpWeXmpqKpUuXIj8/HyZ2TnDx7wWZXPIrNoiIKk2yI1bv3r1x6tQpxMfHqx8dOnRAWFiY+rm+vj527dqlXufChQu4ceMG/P39AQD+/v44deoUUlJS1GV27NgBlUoFb2/vOq8TERHpNrZNuikzMxORkZHIycmBoaUN3Lr3hVwh6dUaRERVJtlRy8zMDK1aaV6Ma2JiAmtra/X8N954AxMmTICVlRVUKhXGjRsHf39/dO7cGQDQt29feHt7Y+TIkZg5cyaSkpLw6aefIiIiAgYGBnVeJyIi0m1sm3TPgwcPEBkZibS0NChNVfAICIFCXyl1WEREVabVPyd99913kMvlGDx4MPLy8hAcHIz58+erlysUCmzcuBFjxoyBv78/TExMEB4eji+++ELCqImIqD5j26Q9CgoKsHz5cqSkpEDP0Bgegf2hZ2gkdVhERNUiE0IIqYOQWkZGBszNzZGens5+/UQSmjNnDjIzMzF69Gg4OjpKHQ7VAR5/y8fX5emKiorw559/4tKlS5DrK9Gk9wswtLCSOizSIXIZMLA52xoqS6pjMK+KJSIiIp0ihMCGDRtw6dIlyBQKuPfox6SMiHQeEzMiIiLSKTt27MCJEycAmQyuXYJgYlvxSMxERLqCiRkRERHpjAMHDiA2NhYA4NwpAKpGbhJHRERUM5iYERERkU44fvw4du7cCQBwaNMZlh7NJY6IiKjmMDEjIiIirXfhwgVs2LABAGDj1Rq2Xr4SR0REVLOYmBEREZFWu379OlatWgUhBCw9POHQupPUIRER1TgmZkRERKS1kpKSsHz5chQWFsKskRsadewOmUwmdVhERDWOiRkRERFppfv37yMyMhJ5eXkwtnWAq39vyOT86kJE9ROPbkRERKR1srKysGTJEmRnZ8PQwhru3YMh19OTOiwiolrDxIyIiIi0Sm5uLpYuXYr79+9DaWIG94AQKJQGUodFRFSrmJgRERGR1igsLERUVBSSkpKgZ2gE98BQ6BsZSx0WEVGtY2JGREREWqG4uBh//fUXrl+/Drm+PtwDQmBgppI6LCKiOsHEjIiIiCQnhMDGjRtx/vx5yOQKuHULhpGljdRhERHVGSZmREREJLndu3fj+PHjgEwGly69YWrvJHVIRER1iokZERERSSo2NhZ///03AKBRh+4wd3aXNiAiIgkwMSMiIiLJnDhxAtu3bwcA2Pt2glUTL4kjIiKSBhMzIiIiksTFixexbt06AIC1pw9sW7SWOCIiIukwMSMiIqI6d/PmTaxcuRJCCFi4NYVjm86QyWRSh0VEJBkmZkRERFSnUlJSsGzZMhQWFsLM0QXOfoFMyoiowdOTOoCGLisrC7du3YKnp6fUoRAREdW6w4cPY/fu3cjLy4OxjT1cu/aBTM7fiYmImJhJKDk5Gb/88gsAIDw8HO7u7tIGREREVIv27t2L6OhoAIDMyARu3YMh1+NXESIigImZZC5cuICoqCj19PK16+EZ8rKEERFJ70FhkdQhEFEtOXv2rDopAwDbJi2gZ2AoXUDU4BULqSMg0sTETAKxsbHqoYEBQN/MnEkZERHVW4mJiVi5cqV62tTJDfat2kkYERGR9mFiVsc2bNiAY8eOqaeN7RzRpNfzAIAzRw5i3W/zcfXMKdy/k4yPfvwNfkEhGuv/c+USlsz+EmePHERRUSGcmzTHxB8WwNbJGZlp9/HnvNk4cWAv7t5OhMrKCp1698PQ9z+CiZmqwpjS7t7Bktlf4cSBvcjOTId3h85449Mv4eTeuHZeBCIiajAyMjKwYMEC9bSBhTU8egSrp7csXYh1v/2MtLt34O7ljTc+/RLNfNtWuL2Nixdg2/LFuHs7EWaWlvAPfg5hEyZB+fDsW2XaUiIibcSrbevQH3/8oZGUWXh4qpMyAMh7kAN3r5Z46/Ovy10/6cY1/Hf4QDRq3BRT/1iFOet24eV3x6sbo/spybiXkoxXP/oc323YjbHT5+L4/mjM/++HFcYkhMA3Ea8j+Z/r+GT+QsxevR22Ts6Y+voryM3JqZmKExFRg5SXl4fvvvtOPa0wMkHzfoPV0wc2r8OiGVMxJGICZq3eBjdPb0x7czjSU++Wu739G1Yj8tuvMSRiAr7ftBfvfvktDmxej6VzZvy7z6e0pURE2opnzOrITz/9hLt3/21o7Hw7wd67jUaZdj16oV2PXhVuY9ncGWgX0AuvTvxMPc/B1V393LW5Fz6a938ay4Z/8DG+nzgORYWFUJRzgfXta1dx8UQcvtuwB67NSkaGHD1lBt7o1hp/b1qDoJfDqlpVIiIiAMCMGf8mTHI9fXgP0GxTNiz6H4JeHo5eg4cCAN6e+g2O7d2FXX8tx6DR48ps7/zxo/Bq1xHdnx8EALBzdkG30IG4dPLfHz2f1pYSEWkrnjGrA7NmzdJIyly69imTlD1NcXEx4qJ3wcm9Mb54YxhGdfHBJ0NCcWjnlieul5OZAWNT03KTMgAoyM8HACgNDNTz5HI59JVKnIs7UqUYiYiISk2bNu3fCZkMLV8apbG8ID8fV86chG+X7up5crkcvv7dcTE+rtxterXtgCtnTuLSyeMAgKSb13Fs3y6069G75itARFTHmJjVsq+++go5j3QJ9Og7CBYuHlXeTnrqXeTmZGPNgh/RtntPfP7bcnQK6odZ497EmcOx5a6TcT8VK3+ei6AhIyrcbqPGTWHj1AiRc6YjKz0NBfn5WLPgR6Qm3cb9O8lVjpOIiOjrr79GcXGxetrnlbfKlMm8fw/FRUWwsLbVmG9uY4O0u3fK3W735wdh6Lj/4NOwgRjSyhURffzRslMXDH7nvZqtABGRBNiVsZbk5+djxowZEOLfsVg9Bw6H0tC0WtsTDxu4jr2C8fxrowEAHi1a4cLxo9gW9QdadvLXKJ+TlYmv334VLk2a45WxFV9jpqevj49++A3zP52AcD9vyBUK+Pp3R9sevQDBcWSJiKhqZs+ejYKCAvV08xfDa2zbpw/FYPX/5uGtz79GM992SLpxDb9//RlWzv8OL7/7QY3th4hICkzMakFWVha+/fZbjXmeg16DUqms9jbNLK2g0NODS9PmGvOdmzTDubjDGvMeZGXhyzeHw9DEBB/9+Bv09PWfuO0mrXzx7dqdyM7MQGFBAcytrPHJkFA0aeVb7XiJiKjhmT9/PrKzs9XTzV8Mh8EjXeUfZWZpBblCgbRUzbNj6XfvwsLGttx1on6YiR4vDFZf/+zm2QK5D3Lwy+cTMfid9yGXsyMQEekuHsFqWHJyskZSJpMr4DN09DMlZQCgr1SiaavWuJVwRWN+4rWrsHVyVk/nZGXiizeGQU9fiUnzF6lHbKwMEzMVzK2skXjtKq6cPoGOvYKfvhIRERGAyMhI3Lnzb5LVNGRQhUkZUNKuNWnpi1Oxf6vnFRcX4+TBv9G8Tfty18l78KBM8lU6LdjLg4h0HM+Y1aALFy4gKipKPa1QGsJ70KuVXv9BdjaSbiSop1P+uYmEc6dham4BWydnDHjjXcyZ8A68O3RGK78uOL5/D47u2YEv/lgF4N+kLO/BA7w/ax5ysrKQk5UFAFBZWUOhUAAAxoV0x4gJ/w9+fUru6xKzdQNUltawcWqEGxfP4fevPkfH3v3Qplvgs74kRETUAGzevBlXrvz7w6Frt74wMrd56nrPvzYa8z4ZjyatWqOZb1tsXLwAeQ9y0GtQySiNP3z8HqzsHDDiw/8HAOjQsw82LPofPFq0QrPW7ZB0PQFRP8xCh5591G3c09pSIiJtxcSshsTGxmL79u3qaX0zc3iFvlKlbVw5fQKTw19STy+aMQUAEDhwCMbNmAu/PiEYPWUGVv/vR/z+1Wdw8miMiT8sQIv2fgCAq2dO4dKJkiGDI/p20dj2zzsPwc7ZBQCQmHAF2ZkZ6mX3U5KxaMYUpKfehYWtHQIHvIyXxoyvUuxERNQwHTx4EEeO/DuKr31rP5g7u1dq3a79ByD9Xiqi5s1C2p078GjREp8uWKruyng38RZksn/PkL00ZjxkMhmWfz8T95KToLKyQoeefTB8/CfqMk9rS4mItJVM8Nw/MjIyYG5ujvT0dKhUqiqvv2nTJhw9elQ9bWznqHHjaCKqnHPrIlH4IAejR4+Go6Oj1OFQHXjW4299pSuvy+M9RSwae8KlU4CEERFVzSBPtjVUllTHYJ4xe0ZLlizB1atX1dMWHp5w8WOjRERE9dudO3c0kjJj+0ZMyoiIngETs2d07do19XO3tp3g6lv+BctE9HRyyKQOgYgqQQiBRYsWqacNVBZo0jNUuoCIiOoBJmbP6OOPP8asWbMwaNAgtGjRQupwiHRavJ4c+VIHQURPJITA1q1bkZOTAwBQGBihef8hEkdFVHVy/hZIWoaJ2TNSKpX473//K3UYREREdWL//v04fLjk/pku/r1g4dZU4oiIiOoH3seMiIiIKuXo0aPYs2cPAMCxXRcmZURENYiJGRERET3V2bNnsWnTJgCAXct2sGneSuKIiIjqFyZmRERE9EQJCQlYvXo1AMCqSQvYteJAV0RENY2JGREREVUoMTERUVFRKCoqgsrZA07tu0Im46gJREQ1jYkZERERlSs1NRVLly5Ffn4+TOyc4OLfCzI5vzoQEdUGHl2JiIiojMzMTCxZsgQ5OTkwsrSBW/e+kCsUUodFRFRvMTEjIiIiDQ8ePEBkZCTS09OhNDOHe0AIFPpKqcMiIqrXmJgRERGRWkFBAZYvX46UlBToGRrDI6A/9AyNpA6LiKjeY2JGREREAICioiKsXLkSN2/ehFxfCY/A/lCamkkdFhFRg8DEjIiIiCCEwPr163Hp0iXIFAq49+gHQwsrqcMiImowmJgRERE1cEIIbN++HSdPngRkMrh27QMTWwepwyIialCYmBERETVwBw4cwMGDBwEAzp0CoHJylTgiIqKGh4kZERFRA3bs2DHs2rULAODQpjMsPZpLHBERUcPExIyIiKiBOn/+PDZu3AgAsG3RBrZevhJHRETUcDExIyIiaoCuX7+OVatWQQgBSw9P2Pt2lDokIqIGjYkZERFRA5OUlITly5ejqKgIqkZuaNSxO2QymdRhERE1aEzMiIiIGpB79+4hMjISeXl5MLF1hIt/b8jk/DpARCQ1HomJiIgaiKysLERGRiI7OxuGFtZw6x4MuZ6e1GERERGYmBERETUIubm5iIyMxP3796E0MYN7QAgUSqXUYRER0UNMzIiIiOq5goICREVFITk5GXqGRnAPDIW+kbHUYRER0SOYmBGR1vrpp5/g7u4OQ0ND+Pn54fDhw08sv3LlSnh5ecHQ0BA+Pj7YvHlzHUVKpL2Ki4vx9ttvY/z48fjyyy+x8I8luJFwpcLyhQUFWPHTHLzbxx9DfT0wYUAQju/fo1HmnV6dMNjLqcxjwReTars6RET1lqSJ2ZQpUyCTyTQeXl5e6uW5ubmIiIiAtbU1TE1NMXjwYCQnJ2ts48aNGwgNDYWxsTHs7OwwceJEFBYW1nVViKiG/fnnn5gwYQImT56MY8eOoXXr1ggODkZKSkq55WNiYjBs2DC88cYbOH78OAYOHIiBAwfi9OnTdRw56bL61i4JIfDxxx/jjz/+QM+evfDF78vg0bI1pr05HOmpd8tdZ/n332DHn5F449MvMXdTNPoOHYmZY9/A1bOn1GW+WbUF/7c/Xv34/PcoAIB/8PN1Ui8iovpI8jNmLVu2xO3bt9WPv//+W73sgw8+wIYNG7By5Urs3bsXiYmJGDRokHp5UVERQkNDkZ+fj5iYGCxevBiLFi3C559/LkVViKgGzZkzB2+99RZGjRoFb29v/PLLLzA2Nsbvv/9ebvnvv/8e/fr1w8SJE9GiRQtMmzYN7dq1w48//ljHkZOuq0/t0q5du7B8+XK0a98eA8ZOhGenbnh76jcwMDTCrr+Wl7vO3nV/YdDb49A+oDccXNzQb1g42vbohQ0Lf1WXMbeyhqWtnfoRF70TDq7uaNnJv66qRkRU70iemOnp6cHBwUH9sLGxAQCkp6fjt99+w5w5c9CrVy+0b98eCxcuRExMDA4ePAgA2L59O86ePYvIyEi0adMGISEhmDZtGn766Sfk5+dLWS0iegb5+fmIi4tDUFCQep5cLkdQUBBiY2PLXSc2NlajPAAEBwdXWJ6oIvWlXYqNjVUnjx37PgeVszuAks+Sr393XIyPK3e9gvx86BsYaMwzMDTEubjyuxIX5Odj3/q/0GvQUN4LjYjoGUiemF26dAlOTk5o3LgxwsLCcOPGDQBAXFwcCgoKNL5oeXl5wdXVVf1FKzY2Fj4+PrC3t1eXCQ4ORkZGBs6cOVPhPvPy8pCRkaHxICLtce/ePRQVFWl8tgHA3t4eSUlJ5a6TlJRUpfJEFakP7dKJEyewfft25OTkQAgB55ZtNJab29gg7e6dctdt0y0AGxb9D4nXrqK4uBgnDuzFwR2bcf9O+d2ID+/aiuzMDPR8ccgzxUxE1NBJevMSPz8/LFq0CJ6enrh9+zamTp2K7t274/Tp00hKSoJSqYSFhYXGOo9+0aroi1jpsopMnz4dU6dOrdnKEBGRzqsP7ZIQAseOHQMAtGrdFgDQ1sEcHV2t1WX2qIxwU6mHgEfmlWo5/0d8EDEG7/fvAZlMBvfGjTHi1XAs+2NxueV/3LQKQX2D8WLHljUSP1Fd4fld0jaSJmYhISHq576+vvDz84ObmxtWrFgBIyOjWtvvpEmTMGHCBPV0RkYGXFxcam1/RFQ5gwcPRlFREWxtbaFQKMoMqpCcnAwHB4dy13VwcKhSeaLy1Id2SSaTISwsDHFxcWjXrh3eev015Kbdg7XRv/csy0i9C2cnR415paxdG2HzhvXIzc1FamoqnJyc8Mknn6Bx48Zlyl+/fh17d+/G6tWry90WERFVnuRdGR9lYWGB5s2b4/Lly3BwcEB+fj7S0tI0yjz6RauiL2KlyypiYGAAlUql8SAi6bm5uaFx48YwMzND+/btsWvXLvWy4uJi7Nq1C/7+5Q8u4O/vr1EeAHbs2FFheaLK0NV2SalUwt/fHwYGBlX+LJUyNDREo0aNUFhYiL/++gsDBgwoU2bhwoWws7NDaGjoM8VLRERalphlZWXhypUrcHR0RPv27aGvr6/RmFy4cAE3btxQNyb+/v44deqUxvDZO3bsgEqlgre3d53HT0Q1Z8KECViwYAEWL16Mc+fOYcyYMcjOzsaoUaMAAK+++iomTfr3nknvv/8+tm7dim+//Rbnz5/HlClTcPToUYwdO1aqKlA9UB/apap+lg4dOoTVq1fj6tWr2L9/P/r164fi4mJ89NFHGtstLi7GwoULER4eDj09STvgEBHVD0JCH374oYiOjhYJCQniwIEDIigoSNjY2IiUlBQhhBDvvPOOcHV1Fbt37xZHjx4V/v7+wt/fX71+YWGhaNWqlejbt6+Ij48XW7duFba2tmLSpElViiM9PV0AEOnp6TVaPyJ6NvPmzROurq5CqVSKTp06iYMHD6qXBQQEiPDwcI3yK1asEM2bNxdKpVK0bNlSbNq0qY4jpqrStuNvfW2XqvJZio6OFi1atBAGBgbC2tpajBw5Uty6davMNrdt2yYAiAsXLtRIjERE2kKqtkkmhBBSJYVDhw7Fvn37kJqaCltbW3Tr1g1fffUVmjRpAqDkRp4ffvghli9fjry8PAQHB2P+/Pka3UGuX7+OMWPGIDo6GiYmJggPD8eMGTOq9OtdRkYGzM3NkZ6ezm6NRER1SNuOv2yXiIhIqmOwpImZtmADSEQkDR5/y8fXhYhIOlIdg7XqGjMiIiIiIqKGiIkZERERERGRxJiYERERERERSYyJGRERERERkcSYmBEREREREUmMiRkREREREZHEmJgRERERERFJjIkZERERERGRxJiYERERERERSYyJGRERERERkcT0pA5AGwghAAAZGRkSR0JE1LCUHndLj8NUgu0SEZF0pGqbmJgByMzMBAC4uLhIHAkRUcOUmZkJc3NzqcPQGmyXiIikV9dtk0zwZ0oUFxcjMTERZmZmkMlkUoejISMjAy4uLrh58yZUKpXU4dQp1r3h1b2h1htouHUXQiAzMxNOTk6Qy9m7vlRl26X69n/D+mg31ke7sT41R6q2iWfMAMjlcjg7O0sdxhOpVKp68SGrDta94dW9odYbaJh155mysqraLtW3/xvWR7uxPtqN9akZUrRN/HmSiIiIiIhIYkzMiIiIiIiIJMbETMsZGBhg8uTJMDAwkDqUOse6N7y6N9R6Aw277lR99e3/hvXRbqyPdmN9dB8H/yAiIiIiIpIYz5gRERERERFJjIkZERERERGRxJiYERERERERSYyJGRERERERkcSYmElk3759eP755+Hk5ASZTIa1a9dqLH/ttdcgk8k0Hv369dMoc+/ePYSFhUGlUsHCwgJvvPEGsrKy6rAWVfe0egsh8Pnnn8PR0RFGRkYICgrCpUuXNMroYr3LM2XKlDLvsZeXl3p5bm4uIiIiYG1tDVNTUwwePBjJyckSRlz7fvrpJ7i7u8PQ0BB+fn44fPiw1CHVCXd39zL/CzNmzNAoc/LkSXTv3h2GhoZwcXHBzJkzJYqWasvTjo/liY6ORrt27WBgYICmTZti0aJFZcpI+bmqyr4DAwPLfA5kMhlCQ0PVZSrTNtamqtRn0aJFZWI1NDTUKFOZNq82VaU+CxYsQPfu3WFpaQlLS0sEBQWVKS/1+wNU/f995cqV8PLygqGhIXx8fLB582aN5VK/R4+q6vefe/fuYdy4cfD09ISRkRFcXV3x3nvvIT09XaNceZ+7qKio2q5Otb7PlXeceOeddzTK3LhxA6GhoTA2NoadnR0mTpyIwsLC2qxKzREkic2bN4v//ve/YvXq1QKAWLNmjcby8PBw0a9fP3H79m314969expl+vXrJ1q3bi0OHjwo9u/fL5o2bSqGDRtWh7WouqfVe8aMGcLc3FysXbtWnDhxQrzwwgvCw8NDPHjwQF1GF+tdnsmTJ4uWLVtqvMd37txRL3/nnXeEi4uL2LVrlzh69Kjo3Lmz6NKli4QR166oqCihVCrF77//Ls6cOSPeeustYWFhIZKTk6UOrda5ubmJL774QuN/ISsrS708PT1d2Nvbi7CwMHH69GmxfPlyYWRkJH799VcJo6aa9rTj4+OuXr0qjI2NxYQJE8TZs2fFvHnzhEKhEFu3blWXkfJzVdV9p6amanwGTp8+LRQKhVi4cKG6TGXaRm2pz8KFC4VKpdKINSkpSaNMZdo8banP8OHDxU8//SSOHz8uzp07J1577TVhbm4u/vnnH3UZKd8fIapepwMHDgiFQiFmzpwpzp49Kz799FOhr68vTp06pS4j5Xv0uKp+/zl16pQYNGiQWL9+vbh8+bLYtWuXaNasmRg8eLBGOQBi4cKFGu9bXdSvOt/nAgICxFtvvaURa3p6unp5YWGhaNWqlQgKChLHjx8XmzdvFjY2NmLSpEm1XZ0awcRMC1SUmA0YMKDCdc6ePSsAiCNHjqjnbdmyRchkMnHr1q1airRmPV7v4uJi4eDgIGbNmqWel5aWJgwMDMTy5cuFEPWj3qUmT54sWrduXe6ytLQ0oa+vL1auXKmed+7cOQFAxMbG1lGEdatTp04iIiJCPV1UVCScnJzE9OnTJYyqbri5uYnvvvuuwuXz588XlpaWIi8vTz3v448/Fp6ennUQHUmhMonZRx99JFq2bKkx75VXXhHBwcHqaSk/V8+67++++06YmZlp/EjxtLaxNlW1PgsXLhTm5uYVbq8ybV5tetb3p7CwUJiZmYnFixer50n5/ghR9ToNGTJEhIaGaszz8/MTb7/9thBC+vfoUTX1/WfFihVCqVSKgoIC9bzKHG9qWnXrExAQIN5///0Kl2/evFnI5XKNH0F+/vlnoVKpNNpQbcWujFosOjoadnZ28PT0xJgxY5CamqpeFhsbCwsLC3To0EE9LygoCHK5HIcOHZIi3GeWkJCApKQkBAUFqeeZm5vDz88PsbGxAOpfvS9dugQnJyc0btwYYWFhuHHjBgAgLi4OBQUFGq+Fl5cXXF1d1a9FfZKfn4+4uDiN+srlcgQFBdXL+pZnxowZsLa2Rtu2bTFr1iyNbhexsbHo0aMHlEqlel5wcDAuXLiA+/fvSxEuaYHY2FiNzwxQ8n9R+pmR8nNVE/v+7bffMHToUJiYmGjMf1LbWFuqW5+srCy4ubnBxcUFAwYMwJkzZ9TLKtPm1ZaaeH9ycnJQUFAAKysrjflSvD9A9er0tM+QlO9RebHWxPef9PR0qFQq6OnpacyPiIiAjY0NOnXqhN9//x2ilm9z/Cz1Wbp0KWxsbNCqVStMmjQJOTk5Gtv18fGBvb29el5wcDAyMjI0Pn/aSu/pRUgK/fr1w6BBg+Dh4YErV67g//2//4eQkBDExsZCoVAgKSkJdnZ2Guvo6enBysoKSUlJEkX9bErjfvTDVDpduqw+1dvPzw+LFi2Cp6cnbt++jalTp6J79+44ffo0kpKSoFQqYWFhobHOo69FfXL37l0UFRWV+96fP39eoqjqznvvvYd27drBysoKMTExmDRpEm7fvo05c+YAKPm/9/Dw0Fin9LVKSkqCpaVlncdM0ktKSir3M5ORkYEHDx7g/v37kn2unvUzffjwYZw+fRq//fabxvyntY21pTr18fT0xO+//w5fX1+kp6dj9uzZ6NKlC86cOQNnZ+dKtXm1pSaOuR9//DGcnJw0khap3h+genWq6DP06HeO0nkVlakrNfH95+7du5g2bRpGjx6tMf+LL75Ar169YGxsjO3bt+Pdd99FVlYW3nvvvRqL/3HVrc/w4cPh5uYGJycnnDx5Eh9//DEuXLiA1atXq7db3vtVukzbMTHTUkOHDlU/9/Hxga+vL5o0aYLo6Gj07t1bwsiopoSEhKif+/r6ws/PD25ublixYgWMjIwkjIxqwieffIJvvvnmiWXOnTsHLy8vTJgwQT3P19cXSqUSb7/9NqZPnw4DA4PaDpVI6/z222/w8fFBp06dNObrUtvo7+8Pf39/9XSXLl3QokUL/Prrr5g2bZqEkT27GTNmICoqCtHR0RoDmujS+6MtKttWPKuMjAyEhobC29sbU6ZM0Vj22WefqZ+3bdsW2dnZmDVrVrUSs9quz6NJpY+PDxwdHdG7d29cuXIFTZo0qfZ2tQUTMx3RuHFj2NjY4PLly+jduzccHByQkpKiUaawsBD37t2Dg4ODRFE+m9K4k5OT4ejoqJ6fnJyMNm3aqMvUt3qXsrCwQPPmzXH58mX06dMH+fn5SEtL0zhrlpycrPP1LI+NjQ0UCkWZUSd1ub4ffvghXnvttSeWady4cbnz/fz8UFhYiGvXrsHT0xMODg7lvjYAdPb1oWdX0f+FSqWCkZERFAqFZJ+rZ/lMZ2dnIyoqCl988cVT9/N421hbauIYpa+vj7Zt2+Ly5csAKtfm1ZZnqc/s2bMxY8YM7Ny5E76+vk8sW1fvD1C9OlX0GSotXxfvUWXbimf5/pOZmYl+/frBzMwMa9asgb6+/hPL+/n5Ydq0acjLy6vyj4N1UZ/HYwWAy5cvo0mTJnBwcCgzEqcutZe8xkxH/PPPP0hNTVUfGPz9/ZGWloa4uDh1md27d6O4uFj9T6prPDw84ODggF27dqnnZWRk4NChQ+pfHetjvUtlZWXhypUrcHR0RPv27aGvr6/xWly4cAE3btzQ+AW2vlAqlWjfvr1GfYuLi7Fr1y6dra+trS28vLye+Hj0mrFHxcfHQy6Xq7t5+Pv7Y9++fSgoKFCX2bFjBzw9PdmNsQHz9/fX+MwAJf8XpZ8ZKT9Xz7LvlStXIi8vDyNGjHjqfh5vG2tLTbyWRUVFOHXqlDrWyrR5taW69Zk5cyamTZuGrVu3alwbVJG6en+A6tXpaZ+huniPKttWVPf7T0ZGBvr27QulUon169eXuWVDeeLj42FpaVmtHhu1XZ/yYgWg8f341KlTGknfjh07oFKp4O3tXeX61DmpRx9pqDIzM8Xx48fF8ePHBQAxZ84ccfz4cXH9+nWRmZkp/vOf/4jY2FiRkJAgdu7cKdq1ayeaNWsmcnNz1dvo16+faNu2rTh06JD4+++/RbNmzbR+2Pgn1VuIkmFpLSwsxLp168TJkyfFgAEDyh0uX9fqXZ4PP/xQREdHi4SEBHHgwAERFBQkbGxsREpKihCiZLh8V1dXsXv3bnH06FHh7+8v/P39JY669kRFRQkDAwOxaNEicfbsWTF69GhhYWFRZnjp+iYmJkZ89913Ij4+Xly5ckVERkYKW1tb8eqrr6rLpKWlCXt7ezFy5Ehx+vRpERUVJYyNjTlcfj3ztOPjJ598IkaOHKkuXzpc/sSJE8W5c+fETz/9VO5w+VJ9rp6275EjR4pPPvmkzHrdunUTr7zySpn5lW0btaU+U6dOFdu2bRNXrlwRcXFxYujQocLQ0FCcOXNGXaYybZ621GfGjBlCqVSKVatWaQxVnpmZKYSQ/v2pTp0OHDgg9PT0xOzZs8W5c+fE5MmTyx0uX6r36HFP+/7zzz//CE9PT3Ho0CEhRMmtVvz8/ISPj4+4fPmyxvtWWFgohBBi/fr1YsGCBeLUqVPi0qVLYv78+cLY2Fh8/vnnWlefy5cviy+++EIcPXpUJCQkiHXr1onGjRuLHj16qNcpHS6/b9++Ij4+XmzdulXY2tpyuHx6sj179ggAZR7h4eEiJydH9O3bV9ja2gp9fX3h5uYm3nrrrTINaWpqqhg2bJgwNTUVKpVKjBo1Sn2A1FZPqrcQJUPTfvbZZ8Le3l4YGBiI3r17iwsXLmhsQxfrXZ5XXnlFODo6CqVSKRo1aiReeeUVcfnyZfXyBw8eiHfffVdYWloKY2Nj8eKLL4rbt29LGHHtmzdvnnB1dRVKpVJ06tRJHDx4UOqQal1cXJzw8/MT5ubmwtDQULRo0UJ8/fXXZb7InDhxQnTr1k0YGBiIRo0aiRkzZkgUMdWWpx0fw8PDRUBAQJl12rRpI5RKpWjcuLHGPb9KSfm5etK+AwIC1HUrdf78eQFAbN++vcy2Kts21qaq1Gf8+PHqsvb29qJ///7i2LFjGturTJtXm6pSHzc3t3L/PydPniyE0I73p6p1EqJk+PjmzZsLpVIpWrZsKTZt2qSxXOr36FFP+/6TkJAgAIg9e/YIISo+pgAQCQkJQoiSIerbtGkjTE1NhYmJiWjdurX45ZdfRFFRkdbV58aNG6JHjx7CyspKGBgYiKZNm4qJEydq3MdMCCGuXbsmQkJChJGRkbCxsREffvihxu0BtJlMiFoeD5OIiIiIiIieiNeYERERERERSYyJGRERERERkcSYmBEREREREUmMiRkREREREZHEmJgRERERERFJjIkZERERERGRxJiYERERERERSYyJGRERERERkcSYmBHVU+7u7pDJZJDJZEhLS2vwcRARUVmBgYEYP378M2+j9DgfHx9fI3FJpbQeFhYWUodCDRATM6InuHPnDsaMGQNXV1cYGBjAwcEBwcHBOHDggLqMTCbD2rVrpQvyCb744gvcvn0b5ubmAIDo6GjIZDJYWloiNzdXo+yRI0fUDVKp0vKlD3t7ewwePBhXr16tdAxHjhzBX3/9VTMVIiJq4F577TUMHDiwxra3evVqTJs27Zm389Zbb+H27dto1apVDURVPdevX4eRkRGysrKqvY3bt29j7ty5NRcUURUwMSN6gsGDB+P48eNYvHgxLl68iPXr1yMwMBCpqalV2k5+fn4tRfhkZmZmcHBw0Ei2SuevWbNGY95vv/0GV1fXcrdz4cIFJCYmYuXKlThz5gyef/55FBUVVSoGW1tbWFlZVa8CRERULQUFBZUqZ2VlBTMzs2fen7GxMRwcHKCnp/fM26qudevWoWfPnjA1Na32NhwcHNQ/ZhLVNSZmRBVIS0vD/v378c0336Bnz55wc3NDp06dMGnSJLzwwgsASrrpAcCLL74ImUymnp4yZQratGmD//u//4OHhwcMDQ3V23zzzTdha2sLlUqFXr164cSJE+p9njhxAj179oSZmRlUKhXat2+Po0ePAij5JfD555+HpaUlTExM0LJlS2zevLladQsPD8fvv/+unn7w4AGioqIQHh5ebnk7Ozs4OjqiR48e+Pzzz3H27FlcvnwZR44cQZ8+fWBjYwNzc3MEBATg2LFj1YqJiIhKrFq1Cj4+PjAyMoK1tTWCgoKQnZ2NKVOmYPHixVi3bp26J0N0dDSuXbsGmUyGP//8EwEBATA0NMTSpUuRmpqKYcOGoVGjRjA2NoaPjw+WL1+usa/HuzK6u7vj66+/xuuvvw4zMzO4urrif//7X5XrUNrjYtu2bWjbti2MjIzQq1cvpKSkYMuWLWjRogVUKhWGDx+OnJwcjXjGjRuH8ePHw9LSEvb29liwYAGys7MxatQomJmZoWnTptiyZUuZfa5bt07dPpeeWfz6669hb28PCwsLfPHFFygsLMTEiRNhZWUFZ2dnLFy4sMp1I6otTMyIKmBqagpTU1OsXbsWeXl55ZY5cuQIAGDhwoW4ffu2ehoALl++jL/++gurV69W97l/+eWX1Y1SXFwc2rVrh969e+PevXsAgLCwMDg7O+PIkSOIi4vDJ598An19fQBAREQE8vLysG/fPpw6dQrffPNNtX8VHDlyJPbv348bN24AAP766y+4u7ujXbt2T13XyMgIQMlZwMzMTISHh+Pvv//GwYMH0axZM/Tv3x+ZmZnViouIqKG7ffs2hg0bhtdffx3nzp1DdHQ0Bg0aBCEE/vOf/2DIkCHo168fbt++jdu3b6NLly7qdT/55BO8//77OHfuHIKDg5Gbm4v27dtj06ZNOH36NEaPHo2RI0fi8OHDT4zh22+/RYcOHXD8+HG8++67GDNmDC5cuFCt+kyZMgU//vgjYmJicPPmTQwZMgRz587FsmXLsGnTJmzfvh3z5s3TWGfx4sWwsbHB4cOHMW7cOIwZMwYvv/wyunTpgmPHjqFv374YOXKkRkKXlpaGv//+W52YAcDu3buRmJiIffv2Yc6cOZg8eTKee+45WFpa4tChQ3jnnXfw9ttv459//qlW3YhqnCCiCq1atUpYWloKQ0ND0aVLFzFp0iRx4sQJjTIAxJo1azTmTZ48Wejr64uUlBT1vP379wuVSiVyc3M1yjZp0kT8+uuvQgghzMzMxKJFi8qNxcfHR0yZMqXSsbu5uYnvvvtOY96ePXsEAHH//n0xcOBAMXXqVCGEED179hTff/+9WLNmjXj0sPBoeSGESExMFF26dBGNGjUSeXl5ZfZZVFQkzMzMxIYNGyrcLxERVSwuLk4AENeuXSt3eXh4uBgwYIDGvISEBAFAzJ0796nbDw0NFR9++KF6OiAgQLz//vvqaTc3NzFixAj1dHFxsbCzsxM///xzhdt8fBtC/Hvc37lzp3re9OnTBQBx5coV9by3335bBAcHa2yrW7du6unCwkJhYmIiRo4cqZ53+/ZtAUDExsaq5y1dulR06NBBPR0eHi7c3NxEUVGRep6np6fo3r17mW0vX75cI/aFCxcKc3PzCutLVFt4xozoCQYPHozExESsX78e/fr1Q3R0NNq1a4dFixY9dV03NzfY2tqqp0+cOIGsrCxYW1urz8aZmpoiISEBV65cAQBMmDABb775JoKCgjBjxgz1fAB477338OWXX6Jr166YPHkyTp48+Ux1e/3117Fo0SJcvXoVsbGxCAsLq7Css7MzTExM4OTkhOzsbPz1119QKpVITk7GW2+9hWbNmsHc3BwqlQpZWVnqM3FERFQ1rVu3Ru/eveHj44OXX34ZCxYswP379yu1bocOHTSmi4qKMG3aNPj4+MDKygqmpqbYtm3bU4/Rvr6+6ucymQwODg5ISUmpemUe25a9vT2MjY3RuHFjjXmPb/vRdRQKBaytreHj46OxDgCN9R7txliqZcuWkMvlGus9up3SbVe3bkQ1jYkZ0VMYGhqiT58++OyzzxATE4PXXnsNkydPfup6JiYmGtNZWVlwdHREfHy8xuPChQuYOHEigJIuH2fOnEFoaCh2794Nb29v9SAdb775Jq5evYqRI0fi1KlT6NChQ5nuH1UREhKCBw8e4I033sDzzz8Pa2vrCsvu378fJ0+eREZGBuLj4+Hn5weg5Fq1+Ph4fP/994iJiUF8fDysra0lG+yEiEjXKRQK7NixA1u2bIG3tzfmzZsHT09PJCQkPHXdx9udWbNm4fvvv8fHH3+MPXv2ID4+HsHBwU89Rpd2oS8lk8lQXFxc9co8ti2ZTFapbZdX5vHtAFCvl5+fj61bt5ZJzJ62nYr2TyQVJmZEVeTt7Y3s7Gz1tL6+fqVGKGzXrh2SkpKgp6eHpk2bajxsbGzU5Zo3b44PPvgA27dvx6BBgzQuTHZxccE777yD1atX48MPP8SCBQuqXQ89PT28+uqriI6Oxuuvv/7Esh4eHmjSpEmZkbsOHDiA9957D/3790fLli1hYGCAu3fvVjsmIiIqSRa6du2KqVOn4vjx41Aqleof6ZRKZaVHxT1w4AAGDBiAESNGoHXr1mjcuDEuXrxYm6FLIjo6GpaWlmjdurXUoRA9EyZmRBVITU1Fr169EBkZiZMnTyIhIQErV67EzJkzMWDAAHU5d3d37Nq1C0lJSU/sbhIUFAR/f38MHDgQ27dvx7Vr1xATE4P//ve/OHr0KB48eICxY8ciOjoa169fx4EDB3DkyBG0aNECADB+/Hhs27YNCQkJOHbsGPbs2aNeVl3Tpk3DnTt3EBwcXK31mzVrhiVLluDcuXM4dOgQwsLC1IODEBFR1R06dAhff/01jh49ihs3bmD16tW4c+eO+njv7u6OkydP4sKFC7h79+4Th8Vv1qwZduzYgZiYGJw7dw5vv/02kpOT66oqdWb9+vVlzpYR6SImZkQVMDU1hZ+fH7777jv06NEDrVq1wmeffYa33noLP/74o7rct99+ix07dsDFxQVt27atcHsymQybN29Gjx49MGrUKDRv3hxDhw7F9evXYW9vD4VCgdTUVLz66qto3rw5hgwZgpCQEEydOhVAybUCERERaNGiBfr164fmzZtj/vz5z1RHpVIJGxubMvc5q6zffvsN9+/fR7t27TBy5Ei89957sLOze6aYiIgaMpVKhX379qF///5o3rw5Pv30U3z77bcICQkBUHIjZ09PT3To0AG2trY4cOBAhdv69NNP0a5dOwQHByMwMBAODg41enNqbcHEjOoLmRBCSB0EEdU8d3d3jB8/XuP+NFKJjo5Gz549cf/+fVhYWEgdDhER1aDAwEC0adMGc+fOrfN9Hzt2DL169cKdO3fKXD9WXYsWLcL48eORlpZWI9sjqiyeMSOqxz7++GOYmpoiPT1dshhatmyp/qWXiIjqp/nz58PU1BSnTp2q0/0WFhZi3rx5NZaUmZqa4p133qmRbRFVFc+YEdVT169fV1970LhxY40hgxtiHEREVDtu3bqFBw8eAABcXV2hVColjqj6Ll++DKBkdEwPDw+Jo6GGhokZEREREf3/duyYAAAAACBY/9ZieLYWADPrGgAAYCbMAAAAZsIMAABgJswAAABmwgwAAGAmzAAAAGbCDAAAYCbMAAAAZgF/Ou87I5ZOegAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "z neutral axis = 185.28\n", + " sigma_s (MPa) = 163\n" + ] + } + ], + "source": [ + "eps1,chiy = calculate_strain_profile(sec,0,m_cuasip*1e6)\n", + "draw_section_response(sec,eps1,chiy)\n", + "# get stress in a reinforced bar\n", + "sigma_s = get_stress_point (sec,50,450,eps1,chiy,0)\n", + "print(' sigma_s (MPa) =',round(sigma_s))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "eps_sm_eps_cm = 0.00062 , where:\n", + " - alfa_e = 22.239\n", + " - hc_eff (mm) = 89\n", + " - rho_p = 0.0602\n", + " - kt = 0.4\n", + " - sigma_s (MPa) = 163\n", + " - fct_eff (MPa) = 2.56\n" + ] + } + ], + "source": [ + "hc_eff = _section_7_3_crack_control.hc_eff(500,450,500-sec.gross_properties.cz)\n", + "Ec_eff = concrete.Ecm/(1+fi)\n", + "alfa_e = _section_7_3_crack_control.alpha_e(200000,Ec_eff)\n", + "rho_p = _section_7_3_crack_control.rho_p_eff(6* math.pi*100, 0 ,0,hc_eff*350) # b=350mm, fi=20mm\n", + "kt = _section_7_3_crack_control.kt('long')\n", + "fct_eff = concrete.fctm\n", + "eps_sm_eps_cm =_section_7_3_crack_control.eps_sm_eps_cm(sigma_s,alfa_e,rho_p,kt,fct_eff,200000)\n", + "print('eps_sm_eps_cm =',round(eps_sm_eps_cm, 5), ', where:')\n", + "print(' - alfa_e =',round(alfa_e,3))\n", + "print(' - hc_eff (mm) =',round(hc_eff))\n", + "print(' - rho_p =',round(rho_p,5))\n", + "print(' - kt =',kt)\n", + "print(' - sigma_s (MPa) =',round(sigma_s))\n", + "print(' - fct_eff (MPa) =',round(fct_eff,2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2.) Crack spacing $S_{r,max}$:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sr_max (mm) 192 , where:\n", + " - c (mm) 40.0\n", + " - fi (mm) 20\n", + " - k1 0.8\n", + " - k2 0.5\n", + " - k3 3.4\n", + " - k4 0.425\n" + ] + } + ], + "source": [ + "# crack spacing\n", + "k1 =_section_7_3_crack_control.k1(bond_type='bond')\n", + "k2 =_section_7_3_crack_control.k2(0)\n", + "k3 =_section_7_3_crack_control.k3()\n", + "k4 =_section_7_3_crack_control.k4()\n", + "c=50-20/2\n", + "fi_rebars=20\n", + "sr_max = _section_7_3_crack_control.sr_max_close(c,fi_rebars,rho_p,k1,k2,k3,k4)\n", + "print('sr_max (mm)',round(sr_max), ', where:')\n", + "print(' - c (mm)',c)\n", + "print(' - fi (mm)',fi_rebars)\n", + "print(' - k1',k1)\n", + "print(' - k2',k2)\n", + "print(' - k3',k3)\n", + "print(' - k4',k4)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3) Crack width $w_k$" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wk (mm) 0.12\n", + "wmax (mm) 0.3\n" + ] + } + ], + "source": [ + "# DGM and CMG: would it be a good idea to wrap this calculations inside the generic_cross_section class?\n", + "# section.wk(...). In some codes it may be not as straightforward to implement (american standards)\n", + "\n", + "wk = _section_7_3_crack_control.wk(sr_max,eps_sm_eps_cm)\n", + "print('wk (mm)',round(wk,2))\n", + "wmax = _section_7_3_crack_control.w_max('XS1','qp')\n", + "print('wmax (mm)', wmax)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/EX3.ipynb b/EX3.ipynb new file mode 100644 index 00000000..402d08aa --- /dev/null +++ b/EX3.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is considered a beam of 13.00 m central span with two cantilevers of 6.00 meters. The cross section is formed by a flange 1.00 meter wide and 0.10 m thick. The web is 0.17 meters wide.\n", + "The total height of the beam is 0.45 m. The beam in the supports is reinforced in the upper face with 8fi16 bars, two of which are within 17 cm of the web.\n", + "\n", + "![beam](EX3.png)\n", + "\n", + "The structure is subject to the following actions:\n", + "- Self weight of the beam\n", + "- Dead load consisting of two railings of 0.5 kN/m weight each, which will be assumed to be located at the edge of the section\n", + "- Live load of 4.0 kN/m²\n", + "Geometric cover of the reinforcements is 30 mm.\n", + "\n", + "It is requested:\n", + "1. Shear reinforcement in critical sections\n", + "2. Shear reinforcement between web and flanges\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from structuralcodes.materials.concrete import ConcreteEC2_2004,ConcreteEC2_2023\n", + "from structuralcodes.materials.reinforcement import ReinforcementEC2_2023, ReinforcementEC2_2004\n", + "from shapely import Polygon\n", + "from structuralcodes.geometry import SurfaceGeometry,CompoundGeometry\n", + "from structuralcodes.sections._reinforcement import (\n", + " add_reinforcement,\n", + " add_reinforcement_line,\n", + ")\n", + "from structuralcodes.sections._generic import GenericSection\n", + "from structuralcodes.plots.section_plots import draw_section\n", + "import math\n", + "from structuralcodes.codes.ec2_2004 import shear" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cross section" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Materials\n", + "fi =2.5\n", + "concrete = ConcreteEC2_2004(25)\n", + "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07) \n", + "# Create section\n", + "poly = Polygon(((0, 0), (1000, 0), (1000, 100), (500+170/2, 100),(500+170/2, 450),(500-170/2, 450),(500-170/2, 100),(0,100)))\n", + "geo = SurfaceGeometry(poly, concrete)\n", + "geo = add_reinforcement_line(geo, (50, 30+16/2), (950, 30+16/2), 16, reinforcemnet,n=8)\n", + "sec = GenericSection(CompoundGeometry([geo]))\n", + "draw_section(sec,\"Section\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$V_{ED}$:\n", + "\n", + "\n", + "\n", + "![Beam](EX3_beamV.png)\n", + "![Beam](EX3_beamM.png)\n", + "\n", + "![Ved_pos](EX3_ved_pos.png)\n", + "![Ved_neg](EX3_ved_neg.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ved left section = 70.9 kN\n", + "Ved right section = 84.0 kN\n" + ] + } + ], + "source": [ + "sw = sec.gross_properties.mass * 10 # kN/m2\n", + "dead_load = 2*0.5 # kN/m2\n", + "live_load = 4 # kN/m2\n", + "cover = 30 # mm\n", + "\n", + "\n", + "q = (1.35*(sw+dead_load)+1.5*(4)) # kN/m\n", + "d = (450-30-16/2)/1000 # m\n", + "ved_left = q*(6-d)\n", + "ved_right = 89.2 - q*d\n", + "print(f'Ved left section = {round(ved_left,1)} kN')\n", + "print(f'Ved right section = {round(ved_right,1)} kN')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "370.8\n", + "Vrd_max = 196 kN\n", + "Required transversal reinf. left section = 1.76 cm2/m\n", + "Required transversal reinf. right section = 2.08 cm2/m\n" + ] + } + ], + "source": [ + "bw=170 #mm\n", + "z = 0.9*(450-30-16/2) #mm\n", + "print(z)\n", + "theta = 21.8 #º \n", + "ac=z*bw\n", + "\n", + "\n", + "# compression strut\n", + "vrd_max= shear.VRdmax(bw,z,concrete.fck,theta,0,ac,concrete.fcd())/1000 # kN\n", + "print(f'Vrd_max = {round(vrd_max)} kN')\n", + "\n", + "# Required transversar reinforcement strut\n", + "# asw_max_s = shear.Asw_max(concrete.fcd(),concrete.fck,bw,1,500/1.15,0,ac) *10 # cm2/m\n", + "asw_left = ved_left /z/500*1e6*1.15/(1/math.tan(math.radians(theta))) /100 # cm2/m\n", + "asw_right = ved_right /z/500*1e6*1.15/(1/math.tan(math.radians(theta))) /100 # cm2/m\n", + "print(f'Required transversal reinf. left section = {round(asw_left,2)} cm2/m')\n", + "print(f'Required transversal reinf. right section = {round(asw_right,2)} cm2/m')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/EX3.png b/EX3.png new file mode 100644 index 00000000..46b7d863 Binary files /dev/null and b/EX3.png differ diff --git a/EX3_beam.png b/EX3_beam.png new file mode 100644 index 00000000..1593ebca Binary files /dev/null and b/EX3_beam.png differ diff --git a/EX3_beamM.png b/EX3_beamM.png new file mode 100644 index 00000000..d564bf61 Binary files /dev/null and b/EX3_beamM.png differ diff --git a/EX3_ved_neg.png b/EX3_ved_neg.png new file mode 100644 index 00000000..77734048 Binary files /dev/null and b/EX3_ved_neg.png differ diff --git a/EX3_ved_pos.png b/EX3_ved_pos.png new file mode 100644 index 00000000..8117333c Binary files /dev/null and b/EX3_ved_pos.png differ diff --git a/Issues.ipynb b/Issues.ipynb new file mode 100644 index 00000000..8602e3b9 --- /dev/null +++ b/Issues.ipynb @@ -0,0 +1,836 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from structuralcodes.codes.ec2_2004 import __materials__\n", + "from structuralcodes.materials.concrete import ConcreteEC2_2004,ConcreteEC2_2023\n", + "from structuralcodes.materials.reinforcement import ReinforcementEC2_2023,ReinforcementEC2_2004\n", + "from shapely import Polygon\n", + "from structuralcodes.geometry import SurfaceGeometry,CompoundGeometry,Geometry\n", + "from structuralcodes.sections._reinforcement import (\n", + " add_reinforcement,\n", + " add_reinforcement_line,\n", + ")\n", + "from structuralcodes.sections._generic import GenericSection\n", + "from structuralcodes.materials.constitutive_laws import Elastic, ElasticPlastic,ParabolaRectangle\n", + "from structuralcodes.plots.section_plots import draw_section,draw_constitutive_law,draw_My_Mz_diagram,draw_section_response,draw_N_M_diagram\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from pprint import pprint\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1) I can not compute gross properties of a secion from a SurfaceGeometry. It must be a CompoundGeometry" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Area = 1750.0 cm2\n" + ] + } + ], + "source": [ + "# Materials\n", + "concrete = ConcreteEC2_2004(25)\n", + "# Create section\n", + "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n", + "geo = SurfaceGeometry(poly, concrete)\n", + "#sec = GenericSection(geo)\n", + "#print(f\"Area = {sec.gross_properties.area/(10**2)} cm2\") # -> Error\n", + "\n", + "sec = GenericSection(CompoundGeometry([geo]))\n", + "print(f\"Area = {sec.gross_properties.area/(10**2)} cm2\") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2) some properties are not working if coordinates are defined negative" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I11 = 364583 cm2\n", + "I11_neg = 364583 cm4\n", + "Sy = 43750 cm3\n", + "Sy_neg = -43750 cm3\n", + "I22 = 178646 cm2\n", + "I22_neg = 178646 cm4\n", + "Sz = 30625 cm3\n", + "Sz_neg = -30625 cm3\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "poly_neg = Polygon(((0, 0), (-350, 0), (-350, -500), (0, -500))) # -500 instead of 500\n", + "geo_neg = SurfaceGeometry(poly_neg, concrete)\n", + "geo_neg = add_reinforcement_line(geo_neg, (50, -50), (300, -50), 0.01, concrete, n=2)\n", + "sec_neg = GenericSection(geo_neg)\n", + "print(f'I11 = {round(sec.gross_properties.i11/(10**4))} cm2') \n", + "print(f'I11_neg = {round(sec_neg.gross_properties.i11/(10**4))} cm4')\n", + "print(f'Sy = {round(sec.gross_properties.sy/(10**3))} cm3') \n", + "print(f'Sy_neg = {round(sec_neg.gross_properties.sy/(10**3))} cm3')\n", + "\n", + "print(f'I22 = {round(sec.gross_properties.i22/(10**4))} cm2') \n", + "print(f'I22_neg = {round(sec_neg.gross_properties.i22/(10**4))} cm4')\n", + "print(f'Sz = {round(sec.gross_properties.sz/(10**3))} cm3') \n", + "print(f'Sz_neg = {round(sec_neg.gross_properties.sz/(10**3))} cm3')\n", + "draw_section(sec)\n", + "draw_section(sec_neg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3) should be \"area\" the total surface area (reinf + concrete)?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "area = 1750 cm2\n", + "area_reinforcement = 22 cm2\n" + ] + } + ], + "source": [ + "# Materials\n", + "concrete = ConcreteEC2_2004(25)\n", + "reinforcement = ReinforcementEC2_2023(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07) \n", + "# Create section\n", + "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n", + "geo = SurfaceGeometry(poly, concrete)\n", + "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 12, reinforcement, n=3)\n", + "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 20, reinforcement, n=6)\n", + "sec = GenericSection(geo, integrator='marin')\n", + "print(f\"area = {round(sec.gross_properties.area/(10**2))} cm2\")\n", + "print(f\"area_reinforcement = {round(sec.gross_properties.area_reinforcement/(10**2))} cm2\")\n", + "# print(f\"area_surface = {round(sec.gross_properties.area_surface/(10**2))} cm2\") DGM: area_surface is no longer defined in the new update" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. I dont undestand why:\n", + "\n", + "- calculate_bending_strength(theta=0).m_z)\n", + "\n", + "is not the same as:\n", + "- calculate_bending_strength(theta=math.pi/2).m_y\n", + "\n", + "or:\n", + "\n", + "- sec.geometry= sec.geometry.rotate(math.pi/2)\n", + "- calculate_bending_strength(theta=0).m_y)\n", + "\n", + "which is the purpose of the m_z result?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====== SECTION ======\n", + "====== Theta = 0 ======\n", + "UltimateBendingMomentResults(theta=0,\n", + " n=-0.0029919976950623095,\n", + " m_y=-562853427.1074414,\n", + " m_z=4.4796883128622224e-08,\n", + " chi_y=-3.909551620178221e-05,\n", + " chi_z=0,\n", + " eps_a=0.00627387905044555)\n", + "====== Theta = pi/2 ======\n", + "UltimateBendingMomentResults(theta=1.5707963267948966,\n", + " n=-0.004536689957603812,\n", + " m_y=-4.6863929542362246e-08,\n", + " m_z=-255115350.12920046,\n", + " chi_y=-2.689176391228454e-05,\n", + " chi_z=0,\n", + " eps_a=0.001206058684649792)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Materials\n", + "concrete = ConcreteEC2_2004(25)\n", + "reinforcement = ReinforcementEC2_2023(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)\n", + "\n", + "# Create section\n", + "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n", + "geo = SurfaceGeometry(poly, concrete)\n", + "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=10)\n", + "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 20, reinforcement, n=10)\n", + "geo = geo.translate(-175,-250)\n", + "sec = GenericSection(geo)\n", + "\n", + "# Draw & print\n", + "print(\"====== SECTION ======\")\n", + "print(\"====== Theta = 0 ======\")\n", + "pprint(sec.section_calculator.calculate_bending_strength(theta=0))\n", + "print(\"====== Theta = pi/2 ======\")\n", + "pprint(sec.section_calculator.calculate_bending_strength(theta=math.pi/2))\n", + "draw_section(sec)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====== ROTATED SECTION ======\n", + "====== Theta = 0 ======\n", + "UltimateBendingMomentResults(theta=0,\n", + " n=-0.004536689957603812,\n", + " m_y=-255115350.12920046,\n", + " m_z=2.751732939644625e-08,\n", + " chi_y=-2.689176391228454e-05,\n", + " chi_z=0,\n", + " eps_a=0.001206058684649792)\n", + "====== Theta = pi/2 ======\n", + "UltimateBendingMomentResults(theta=1.5707963267948966,\n", + " n=-0.0029919976950623095,\n", + " m_y=-7.926171552343454e-08,\n", + " m_z=-562853427.1074414,\n", + " chi_y=-3.909551620178221e-05,\n", + " chi_z=0,\n", + " eps_a=0.00627387905044555)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Rotated section\n", + "rot_sec = GenericSection(geo.rotate(math.pi/2))\n", + "print(\"====== ROTATED SECTION ======\")\n", + "print(\"====== Theta = 0 ======\")\n", + "pprint(rot_sec.section_calculator.calculate_bending_strength(theta=0))\n", + "print(\"====== Theta = pi/2 ======\")\n", + "pprint(rot_sec.section_calculator.calculate_bending_strength(theta=math.pi/2))\n", + "draw_section(rot_sec)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Sign criteria in section class:\n", + "\n", + "I am assuming z-axis downward. By default (theta=0) \"calculate_bending_strength\" gets negative value if \"theta=0\" correspondig with My_min; and positive if theta = pi/2 (My_max). It seem strange to me. At least it should be documented. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My_meg [mkN]=-69\n", + "My_pos [mkN]=324\n" + ] + } + ], + "source": [ + "# Materials\n", + "concrete = ConcreteEC2_2004(25)\n", + "reinforcement = ReinforcementEC2_2023(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07) \n", + "# Create section\n", + "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n", + "geo = SurfaceGeometry(poly, concrete)\n", + "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n", + "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcement, n=3)\n", + "sec = GenericSection(geo)\n", + "draw_section(sec)\n", + "\n", + "print(f\"My_meg [mkN]={round(sec.section_calculator.calculate_bending_strength(theta=0).m_y/1000**2)}\")\n", + "print(f\"My_pos [mkN]={round(sec.section_calculator.calculate_bending_strength(theta=math.pi).m_y/1000**2)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "6. Error when computing bending capacity with external axial load n<>0 (#1) or the section response for a given N-M (#2)\n", + "\n", + "(if center of gravity is in the origin it works more or less ok.)\n", + "\n", + "![N-M](error_N_M.png)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N=-3500.00 N\t M=-207.35 kNm\n", + "N=-3000.00 N\t M=-289.86 kNm\n", + "N=-2500.00 N\t M=-343.26 kNm\n", + "N=-2000.00 N\t M=-369.23 kNm\n", + "N=-1500.00 N\t M=-340.02 kNm\n", + "N=-1000.00 N\t M=-262.75 kNm\n", + "N=-500.00 N\t M=-166.01 kNm\n", + "N=0.00 N\t M=-65.78 kNm\n", + "N=500.00 N\t M=35.05 kNm\n", + "N=900.00 N\t M=118.06 kNm\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Materials\n", + "concrete = ConcreteEC2_2004(25)\n", + "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07) \n", + "\n", + "# Create section\n", + "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n", + "geo = SurfaceGeometry(poly, concrete)\n", + "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcement, n=3)\n", + "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n", + "geo = geo.translate(-175, -250) # Translate the section\n", + "sec = GenericSection(geo)\n", + "\n", + "# Compute bending strength for a list of axial loads\n", + "for n_ed in [-3500, -3000, -2500, -2000, -1500, -1000, -500, 0, 500, 900]:\n", + " m_y = sec.section_calculator.calculate_bending_strength(n=n_ed*1e3).m_y/1e6\n", + " print(f\"N={n_ed:.2f} N\\t\", f\"M={m_y:.2f} kNm\")\n", + "\n", + "draw_section(sec)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "z neutral axis = 1193.69\n" + ] + } + ], + "source": [ + "from SLS_section_response import calculate_strain_profile\n", + "from structuralcodes.plots.section_plots import draw_section_response, draw_N_M_diagram\n", + "concrete = ConcreteEC2_2004(25)\n", + "reinforcement = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07) \n", + "# Create section\n", + "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n", + "geo = SurfaceGeometry(poly, concrete)\n", + "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcement, n=3)\n", + "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n", + "geo = geo.translate(-175,-250)\n", + "sec = GenericSection(geo)\n", + "draw_section(sec)\n", + "\n", + "# 1\n", + "n_ = [-3500,-3000,-2500,-2000,-1500,-1000,-500,0,500,900] # kN\n", + "for n_ed in n_:\n", + " print(f\"N={n_ed}\",f\"M={sec.section_calculator.calculate_bending_strength(n=n_ed*1e3).m_y/1e6}\")\n", + "\n", + "\n", + "# 2\n", + "my_ed =0 # mkN\n", + "n_ed=-500 # kN\n", + "#eps,chi= calculate_strain_profile(sec,n_ed,my_ed)\n", + "#draw_section_response(sec,eps,chi)\n", + "eps,chi= calculate_strain_profile(sec,n_ed*1e3,my_ed*1e6)\n", + "print(f\"eps={eps}\")\n", + "print(f\"chi={chi}\")\n", + "draw_section_response(sec,eps,chi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Error copmputing bending stress with axial load comes from \"integrate_strain_response_on_geometry\" since the strain profile is validated\n", + "\n", + "\n", + "![N-M](N2000_my_min.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epsa= -0.0003794534712475606 similar to 2.703e-3 (validation result)\n", + "chi= -1.248218611500977e-05 similar to 12.3e-6 (validation result)\n", + "my = -369 <> 348.85 (validation result)\n" + ] + } + ], + "source": [ + "strain = sec.section_calculator.find_equilibrium_fixed_pivot(sec.geometry, n=-2000*1e3)\n", + "print(f'epsa= {strain[0]} similar to 2.703e-3 (validation result)' )\n", + "print(f'chi= {strain[1]} similar to 12.3e-6 (validation result)' )\n", + "print(f'my = {round(sec.section_calculator.calculate_bending_strength(0, n=-2000*1e3).m_y/1e6)} <> 348.85 (validation result)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "7. How can we obtain the material of a part of a section? We only have access to the constitutive law of each geometry but not to the material. It could be useful to get for example if the constitutive law of concrete was created with gamma=1 or gamma=1.5 (maybe constitutive_law should have this attribute). It could also be important to store other attributes of the original material\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "8. I miss information about the units in the docstrings within the functions of the section classes.\n", + "- Force: N\n", + "- Moment: N*mm\n", + "- Dimensions: mm\n", + "- curvature: 1/mm\n", + "\n", + "Also the sign criteria of internal moments in cross section, eps and curvature\n", + "- section_calculator.calculate_bending_strength(0).m_y -> returns a negative value. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "9. My-Mz diagram:\n", + "Small discrepancies in N=0. If axial load, something goes wrong.\n", + "\n", + "N= 0 kN\n", + "\n", + "![my-Mz](My_Mz_N_0kN.png)\n", + "\n", + "\n", + "N= -500 kN\n", + "\n", + "![my-Mz](My_Mz_N_-500kN.png)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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27nCzdoOtrm2FHpZOCJGczBXjwkIcFhYGf39/aGtrl7tO4YjoL1uxpdHR0QEAXLt2DQkJCcVGahNSnbLysvA84bm44Bb28abmlDwewUTDRNzatTewh72+PSy1LMHnSXahBJ/Hh4OxAxyMHeDd0xshiSHwDfGFz2sf3Ht/Dw9iH+BB7AP8du03WGlZwd3aHW42buhg3AECvqAqD52QOk3qinF6ejrCw8PF7yMjIxEUFAQtLS0YGhri+++/x+PHj3HmzBkIhULExxecYtPS0oKCggICAwPx4MEDdOrUCfXr10dERAQWLFgAS0tLcas4JiYGTk5O2Lt3L9q1awcA2LVrF5o0aQJdXV3cvXsXM2bMwKxZs2BtbV3zHwKp9RhjiE+PL9baDfkUAhErfjmdgkABTXWbigtuYatXS1mrSnNZ61jjF51f8EvHXxCfHo/TIafhE+KDK2+uIDwpHH/f/Rt/3/0buiq6cGnsAncbd/Rs2BPK8spVmoOQukbqivHDhw/RvXt38fuffvoJADB69GgsWrQIfn5+AIAWLVoUWc/f3x/dunWDiooKTp48iYULFyIjIwOGhobo27cv5s+fLz6lnJeXh5CQEGRmZorXDwkJwbx585CUlARzc3P89ttvRS6RIqSy8oR5eJ34ukhrNyg+CB8zP5a4vK6KbrHWro2ODeQFJV8NUF0M1AwwofUETGg9AWk5abgYcRE+r31wNuwsPmZ+xM6gndgZtBMq8iroY9kH7jbucG7kDG2V8s9WEUKKkuoBXLKmMp32dV1tG8D1OetzkQFVT+Of4sXHF8gV5hZbls/jw1rbukhrt4VBCxioGUh132yeMA83392Ez2sf+Ib4Ijo1WjxPwBOgs1ln8els83rm3AUlhCOVqQVUjKsQFeOKk9ViLGIiRCRFFGvtflmYvqSuoC4uui0MWsBe3x5N9ZpCRV6lhpNXLcYYnsQ/ERfm4A/BRebb69vD3cYd7jbusNe3l+o/MgipKlSMOUbFuOJkoRhn5GbgWcKzgtbu//fvPkt4hvTc9BKXN69nLi64hcXXvJ55nShEbz6/ge9rX/iG+OJW1K0i/d9mmmZws3aDu407Opt1hhxf6nrJCKkSVIw5RsW44qSpGDPG8D71fbHWbnhSOBiK/zNRklOCnZ5dkdZuc/3mdNOM/5eYmYizoWfhE+KDi+EXkZWfJZ5XX6k+vmv8Hdxt3NHbsnexy64IkWV15jpjQr5VrjAXLz++LNLaffrhKZKykkpc3kDNoEhr197AHo21G1Prrgw6KjoY3WI0RrcYjcy8TFx5cwU+r31wOvQ0EjMTsS94H/YF74OiQBG9LHvBzdoNLo1doK+mz3V0Qmoc/SYhtd7HjI/FWruvEl8hX5RfbFkBT4Amuk2KtHbtDeyhp6rHQfLaQ0VeBa7WrnC1doVQJMSd6Dvwee0DnxAfvPn8BmdCz+BM6BnwwIOjiaP4dHYj7UZcRyekRtBp6ipEp6krrjpPU9+JvoP51+bD/61/ifPrKdUr1tq11bWFkpxSleYgpWOM4cXHF+IBYA9jHxaZb6trKy7MbYzaSHwzE0K4RKepCQHwOO4x5l+bj/Ph58XTrLSsigyosjewh4mGSZ0YVCXNeDwe7PTsYKdnh/ld5iM6JRp+IX7wDfGF/1t/vPz4Ei8/voR3gDeM1I3g2tiVnjRFaiVqGVchahlXXFW2jF8kvMDv13/HyVcnARScch7Xchzmd5kPU03TqohLalBydjLOhZ2Db4gvzoWdKzJ6XUNRA/2s+tGTpohUopYxqZPCk8Kx6PoiHHx2EAwMPPDg0cwDi7otgpWWFdfxSCXVU6oHj2Ye8GjmgZz8HFyLvAbfEF/xk6aOvDiCIy+O0JOmSK1ALeMqRC3jivuWlnFUShT+uPEHdgXtgpAJAQCDmgzC4m6L0VSvaXXEJVJAxETFnjT1JY9mHljWYxnM6plxlJDUddQyJnVCfHo8lt1ahq2PtopvM9m/UX8s6bYErY1ac5yOVLeynjR19/1dHHx2ECdensCM9jMwr/M81FOqx3VkQspFQxOJzPiU+QlzLs9Bw7UNsf7+euQKc9HNvBsCxgbgrMdZKsR1lLWONX7p+AvujL+DR16P0N28O3KEOVh5ZyWs1llhfeD6Eu8NTog0oWJMpF5KdgoWXV8Ei7UWWHlnJbLys+Bg7ICro67Cf7Q/Opp25DoikRKtDFvh6qirODP8DJroNMGnrE+YfmE67DbZ4dSrU6BeOSKtqBgTqZWRm4EVASvQcF1DLL6xGGm5aWhh0AJnhp/BnXF30MOiB9cRiRTi8XhwbuyM4MnB2OK8BXqqeghLCsPAowPReVdnBL4P5DoiIcVQMSZSJzs/G+sC18FynSXmXp2LpKwk2OjY4Oj3R/HI6xGcGzvT9cGkXHJ8OUxsMxHhP4Zjfuf5UJZTxu3o23DY4YBhx4ch8nMk1xEJEaNiTKRGnjAP2x5tQ6P1jTDjwgx8yPiAhvUbYq/7Xjyf/ByDmw6mOzCRClNXVMcfPf5A6I+hGNNiDHjg4ciLI7DZaIOfL/2Mz1mfuY5ICBVjIh3Ohp6FzUYbTDwzEe9T38NYwxhbv9uK11Nfw9PeEwK+gOuIRMYZaxhjl9suPJn4BD0b9kSuMBer7q6C5TpLrLm3hgZ5EU5RMSacETERrry5AgD4/frvePP5DfRU9bCmzxqE/RgGr9ZekBfIc5yS1Db2Bva4NPISzo84j6a6TfE5+zNmXZwF2422OP7yOA3yIpygYkxqHGMMZ0PPos22NphzZQ4AQF1BHd5O3ngz/Q1mOMyghzWQasXj8dDXqi+CJgVhu8t2GKgZIOJzBAYfG4yOOzvibvRdriOSOoaKMalR1yKvoePOjvju0Hd4Ev8EynLKAIDTw09jbqe5UFVQ5TghqUvk+HL4odUPCPsxDAu7LoSKvAruvr8Lx52OGHxsMCKSIriOSOoIKsakRtyNvgunvU5w2uuEu+/vQllOGbMdZ+OMxxkABYNsCOGKmoIaFnVbhLAfwzC+5XjwwMPxl8fRZGMTzLowC58yP3EdkdRyVIxJtXoS9wTOB53huNMR1yKvQZ4vj2ltpyFiegRW9lpJtyokUsVI3Qj/uv6LoElB6GPZB3miPKwJXAOr9VZYdWcVcvJzuI5IaikqxqRavPz4EoOPDUarba1wLuwcBDwBfmhZcDpwff/1MFT/tsclElKdmus3x4WRF3Bx5EU012+O5Oxk/Hz5Z9hstMGR50dokBepclSMSZWKSIqA5ylP2G2yw/GXx8WPM3w19RW2u26nJ+kQmdLbsjceez3GTtedMFI3wtvktxh2Yhg67OiAgKgAruORWoSKMakS0SnR8DrtBesN1tgfvB8MDAObDETw5GAcGHgAjbQbcR2RkEoR8AUY23IsQqeFYkm3JVCVV0VgTCA67+qMQUcHIexTGNcRSS1AxZh8k/j0eMw4PwNW662w/fF2CJkQ/az64eGEhzgx5ATs9Oy4jkhIlVBVUMWCrgsQPj0cXq28wOfxcfLVSdhussX089ORmJnIdUQiw6gYk0pJykrC3CtzYbnOEuvur0OuMBddzbri1thbODfiHD3OkNRaBmoG2OqyFcGTguHcyBn5onysv78elusssfL2SmTnZ3MdkcggKsakQlJzUrH4+mJYrLXAitsrkJmXifYN2uOK5xX4j/ZHJ9NOXEckpEY01WuKMx5ncMXzCloYtEBqTirmXJkD6w3WOPjsIERMxHVEIkNkshinpaVh5syZMDMzg7KyMhwdHfHgwYMiy7x69Qqurq7Q1NSEqqoq2rZti6ioKIm2f/jwYfB4PLi7u1dDetmUkZuBlbdXwmKtBRbdWITUnFTY69vDb5gf7o6/C6eGTvQkJVInOTV0wiOvR9jjvgcN1BsgKiUKI06OQPt/2+PG2xtcxyMyQiaL8Q8//IDLly9j3759ePbsGXr37o2ePXsiJiYGABAREYFOnTrBxsYG169fR3BwMBYsWAAlpfJvsfj27Vv8/PPP6Ny5c3UfhkzIyc/B+sCCU3BzrsxBUlYSrLWtceT7I3g88TFcrF2oCJM6j8/jY5T9KIT+GIqlPZZCTUEND2MfotuebnA77IbXia+5jkikHI/J2AVzWVlZUFdXh6+vL5ydncXTW7dujX79+uHPP//EsGHDIC8vj3379lVo20KhEF26dMG4ceNw69YtJCcnw8fHR+L1U1NToampiZSUFGhoaFRo39ImT5iHPU/3YMmNJYhOjQYAWNSzwKJui+DRzANyfLkq2U9cXBy2bdsGLy8vGBrStcekdviQ/gGLbyzGtkfbIGRCCHgCTGw9EQu7LYSeqh7X8Ug1q0wtkLmWcX5+PoRCYbFWrrKyMgICAiASiXD27Fk0btwYffr0gZ6eHtq3by9RUV2yZAn09PQwfvx4ibLk5OQgNTW1yKs2eJv8FvZb7DHh9AREp0ajgXoDbHHegtfTXmOU/agqK8SE1Fb6avrY5LwJz6c8h6u1K4RMiE0PN8FqnRW8b3kjKy+L64hEyshcMVZXV0eHDh3wxx9/IDY2FkKhEPv378fdu3cRFxeHhIQEpKenY/ny5ejbty8uXbqEAQMGYODAgbhxo/T+m4CAAOzYsQPbt2+XOIu3tzc0NTXFLxMTk6o4RE6FfgpF512d8SrxFXRVdPFPn38QPj0cE9tMhIJAget4hMgUGx0b+A7zhf9of7Q2bI203DT8eu1XWG+wxr6n+2iQFxGTuWIMAPv27QNjDA0aNICioiLWrVuH4cOHg8/nQyQq+HK7ublh1qxZaNGiBebOnYvvvvsOW7ZsKXF7aWlp8PT0xPbt26GjoyNxjnnz5iElJUX8io6OrpLj48rzhOfosqsL3qe+RxOdJgiaFISZDjPpcYaEfKNu5t1wf8J97B+wH6aapohOjcYon1Fw+NcBLz++5DoekQIyWYwtLS1x48YNpKenIzo6Gvfv30deXh4aNmwIHR0dyMnJwdbWtsg6TZo0KXU0dUREBN6+fQsXFxfIyclBTk4Oe/fuhZ+fH+Tk5BARUfJj1BQVFaGhoVHkJasexz1Gt93d8CHjA+z17XF9zHUYqRtxHYuQWoPP42NE8xF4PfU1ljsth4aiBh7EPkCrra2w5t4aaiXXcTJZjAupqqrC0NAQnz9/xsWLF+Hm5gYFBQW0bdsWISEhRZYNDQ2FmVnJ90W2sbHBs2fPEBQUJH65urqie/fuCAoKqhWnn8tyN/oueuzpgU9Zn9DWqC2ujb5Gg0wIqSbK8sqY02kOXk19hb5WfZEjzMGsi7PQc29PRKVIdvklqX1kciTOxYsXwRiDtbU1wsPDMXv2bNjY2GDs2LEAgNmzZ2Po0KHo0qULunfvjgsXLuD06dO4fv26eBujRo1CgwYN4O3tDSUlJdjZFb1tY7169QCg2PTa5vrb6/ju4HfIyMtAJ9NOOOtxFhqKstvCJ0RWGKkb4ZzHOWx9tBX/u/Q/+L/1R7PNzbC+33p4NvekSwbrGJlsGaekpGDq1KmwsbHBqFGj0KlTJ1y8eBHy8vIAgAEDBmDLli1YuXIlmjVrhn///RcnTpxAp07/3R0qKioKcXFxXB2CVLgYfhH9DvRDRl4GejbsiQsjLlAhJqQG8Xg8TGozCUETg+Bg7IDUnFSM9hmN7499j48ZH7mOR2qQzF1nLM1k6Tpj39e+GHJ8CHKFufiu8Xc4NvgYJwO16DpjQgrki/KxImAFFt1YhHxRPvRV9fGv67/4rvF3XEcjFVQnrjMm3+7I8yMYdHQQcoW5+N72e5wYcoJGTBPCMTm+HH7r8hvu/3Aftrq2+JDxAS6HXDDBbwLSctK4jkeqGRXjOmZ30G54nPSAkAnh2dwThwYdouuHCZEiLQ1b4pHXI/zk8BN44OHfJ//Cfos9AqICuI5GqhEV4zpk84PNGOs7FiImglcrL+x230130yJECinJKWFVn1W4NvoazDTNEJkciS67umDO5TnIyc/hOh6pBlSM64hVd1ZhyrkpAIAZ7Wdgy3dbwOfRj58QadbNvBuCJwdjTIsxYGBYeWcl2v3bDsEfgrmORqoY/Tau5Rhj+OPGH/j58s8AgF87/Yp/+vxDl00QIiM0FDWwy20XTg09BV0VXQR/CEabbW2wImAFhCIh1/FIFaFiXIsxxvDr1V/x+/XfAQB/dv8TS52WUiEmRAa527iLHzyRJ8rD3Ktz0XV3V7z5/IbraKQKUDGupRhjmHlhJpbfXg4AWN17NX7r8hvHqQgh30JPVQ8+Q32w03Un1BTUcDv6Nppvbo7tj7aDrlKVbVSMayGhSIiJZyZi3f11AIDNzpsxq8MsjlMRQqoCj8fD2JZjETwpGF3MuiAjLwNeZ7zgcsgF8enxXMcjlUTFuJbJF+VjjO8YbH+8HXweH7vddmNSm0lcxyKEVDGL+ha4Nuoa/ur1FxQECjgbdhZ2m+xw4uUJrqORSqBiXIvkCnMx7Pgw7A/eDzm+HA4NOoTRLUZzHYsQUk0EfAF+dvwZj7wewV7fHp+yPuH7Y99j1KlRSMlO4ToeqQAqxrXIiJMjcOLVCSgIFHBiyAkMaTqE60iEkBpgp2eH+xPuY16neeDz+NgXvA/NNjfDtchrXEcjEqJiXEsEvg/E8ZfHIc+Xx+nhp+Fq7cp1JEJIDVIQKGCZ0zLcGnsLlvUtEZ0aDae9Tph5YSay8rK4jkfKQcW4llgbuBYA4NHMA70te3OchhDCFUcTRwRNCsLE1hMBFPxuaL2tNR7FPuI4GSkLFeNaICY1BsdeHgNQcHctQkjdpqaghi3fbcE5j3MwUDPAq8RXcNjhgCU3liBflM91PFICKsa1wOaHm5Evykdn085oadiS6ziEECnRr1E/PJ/8HINtByNflI+F1xei486OCEkM4Toa+QoVYxmXnZ+NrY+2AqBWMSGkOG0VbRz5/gj2D9gPTUVN3I+5j5ZbW2Lj/Y10oxApQsVYxh18dhCJmYkw1TSFm40b13EIIVKIx+NhRPMReDb5GZwsnJCVn4Vp56ehz/4+iEmN4ToeARVjmcYYEw/cmtZ2Gj0OkRBSJhNNE1zyvIR1fddBSU4Jl99cht1mOxx6dojraHUeFWMZdv3tdQR/CIaKvAp+aPUD13EIITKAz+Pjx/Y/4snEJ2hr1BbJ2cnwOOmBYceHISkriet4dRYVYxlW2Coe1XwU6ivX5zgNIUSW2OjY4Pa421jUdREEPAGOvDgCu012uBxxmetodRIVYxn15vMb+IX4AQCmt5/OcRpCiCySF8hjYbeFuPfDPdjo2CAuPQ59D/TFlodbuI5W51AxllEb7m8AA0Nvy95ootuE6ziEEBnWxqgNHns9xtgWYyFiIkw+OxnzrsyDiIm4jlZnUDGWQWk5adjxZAcAupyJEFI1lOWVscN1BxZ3WwwAWH57OTxPeSInP4fjZHUDFWMZtOfpHqTmpKKxdmP0terLdRxCSC3B4/Hwe9ffsdttN+T4cjj47CD6HuiL5OxkrqPVelSMZQxjDBvubwAA/NjuR/B59CMkhFSt0S1G45zHOagrqOP62+votLMTolKiuI5Vq9FvchnzNvktQj6FQJ4vj9H29KxiQkj16GXZC7fG3oKRuhFefHwBh38dEBQfxHWsWouKsYy5+e4mgIIBF+qK6hynIYTUZvYG9rg3/h7s9OwQlx6Hzrs642L4Ra5j1UpUjGXMrahbAIDOpp05TkIIqQtMNE1wa+wtdDfvjvTcdDgfdMauJ7u4jlXryGQxjomJwciRI6GtrQ1lZWU0a9YMDx8+FM9PT0/HtGnTYGxsDGVlZdja2mLLlvKvm1uzZg2sra2hrKwMExMTzJo1C9nZ2dV5KBVWWIy7mHXhOAkhpK6op1QPF0ZewMjmIyFkQozzG4dF1xfRgyaqkMzdzPjz58/o2LEjunfvjvPnz0NXVxdhYWGoX/+/O1D99NNPuHbtGvbv3w9zc3NcunQJU6ZMgZGREVxdXUvc7sGDBzF37lzs3LkTjo6OCA0NxZgxY8Dj8bB69eqaOrwyxafHI/RTKHjgoaNpR67jEELqEAWBAva674WphimWBSzD4huLEZUSha3fbYW8QJ7reDJP5orxihUrYGJigl27/jtNYmFhUWSZO3fuYPTo0ejWrRsAwMvLC1u3bsX9+/dLLcZ37txBx44d4eHhAQAwNzfH8OHDERgYWD0HUgkBUQEAgGb6zVBPqR63YQghdQ6Px8NSp6Uwq2eGKWenYFfQLsSkxeDY4GPQUNTgOp5Mk7nT1H5+fmjTpg0GDx4MPT09tGzZEtu3by+yjKOjI/z8/BATEwPGGPz9/REaGorevXuXul1HR0c8evQI9+/fBwC8efMG586dQ//+/UtdJycnB6mpqUVe1enWu/8/RW1Kp6gJIdzxau0Fv+F+UJFXwaWIS+iyqwti02K5jiXTZK4Yv3nzBps3b0ajRo1w8eJFTJ48GdOnT8eePXvEy6xfvx62trYwNjaGgoIC+vbti40bN6JLl9KLmIeHB5YsWYJOnTpBXl4elpaW6NatG3799ddS1/H29oampqb4ZWJiUqXH+rWbUQUjqTub0eAtQgi3+jfqjxtjbkBPVQ9PPzyFw78OeJHwgutYMkvmirFIJEKrVq2wbNkytGzZEl5eXpgwYUKRAVrr16/HvXv34Ofnh0ePHmHVqlWYOnUqrly5Uup2r1+/jmXLlmHTpk14/PgxTp48ibNnz+KPP/4odZ158+YhJSVF/IqOjq7SY/1SSnYKnsY/BUAjqQkh0qGNURvcG38P1trWiE6NRsedHeEf6c91LJkkc33GhoaGsLW1LTKtSZMmOHHiBAAgKysLv/76K06dOgVnZ2cAQPPmzREUFIS///4bPXv2LHG7CxYsgKenJ374oeC5wM2aNUNGRga8vLzw22+/gc8v/neLoqIiFBUVq/LwSnU7+jYYGKy0rGCoblgj+ySEkPJY1LfAnfF34HbYDQFRAeizvw92ue3CiOYjuI4mU2SuZdyxY0eEhIQUmRYaGgozMzMAQF5eHvLy8ooVT4FAAJGo9CeQZGZmlrgOAKkYvl/YX0ytYkKItNFS1sJlz8sY0nQI8kR5GHlqJLxveUvF705ZIXMt41mzZsHR0RHLli3DkCFDcP/+fWzbtg3btm0DAGhoaKBr166YPXs2lJWVYWZmhhs3bmDv3r1FLlEaNWoUGjRoAG9vbwCAi4sLVq9ejZYtW6J9+/YIDw/HggUL4OLiIi7KXKKbfRBCpJmSnBIODToEEw0TrLq7Cr9e+xVRKVFY33895PgyV2pqnMx9Qm3btsWpU6cwb948LFmyBBYWFlizZg1GjPjvlMjhw4cxb948jBgxAklJSTAzM8PSpUsxadIk8TJRUVFFWsLz588Hj8fD/PnzERMTA11dXbi4uGDp0qU1enwlycrLwv2YglHedLMPQoi04vP4+Lv33zDTNMOMCzOw5dEWvE97j8ODDkNVQZXreFKNx+g8QpVJTU2FpqYmUlJSoKFRddfcPYh5gHb/toOeqh7i/xcPHo9XZdvmWlxcHLZt2wYvLy8YGlJfOCG1xalXp+Bx0gPZ+dloY9QGZ4afgb6aPtexakRlaoHM9RnXRW+T3wIArLSsalUhJoTUXgOaDMC1UdegrayNh7EP0WFHB4QkhpS/Yh1FxVgGFD5H1FTTlOMkhBAiuQ4mHXB3/F1Y1rdEZHIkHHc64nbUba5jSSUqxjLgXco7AICZphnHSQghpGIaaTfC3fF30b5BeyRlJcFprxOOvzzOdSypQ8VYBlDLmBAiy3RVdXFt9DW4WbshR5iDIceG4J+7/3AdS6pQMZYB1DImhMg6FXkVnBhyAtPaTgMDw0+XfsLMCzMhFAm5jiYVqBjLAGoZE0JqAwFfgHX91uGvXn8BANYGrsXgY4ORlZfFcTLuUTGWcum56UjKSgIAmNWjljEhRLbxeDz87PgzDg86DAWBAk69PgWnvU5IzEzkOhqnqBhLucJWsaaiJj0vlBBSawy1G4rLnpdRT6ke7r6/C8cdjohIiuA6FmeoGEu5d8n/319MrWJCSC3TxawL7oy7AzNNM4QlhaHDjg7iuw3WNVSMpRz1FxNCarMmuk1w74d7aGXYCh8zP6Lb7m7wC/HjOlaNo2Is5WgkNSGktjNQM8CNMTfQv1F/ZOVnYcCRAdj7dC/XsWoUFWMpRy1jQkhdoKagBt9hvpjQagJETIRJZybhdeJrrmPVGCrGUq6wGFPLmBBS28nx5bDluy3o1bAXsvKz4HnKE3nCPK5j1QgqxlKu8DQ1tYwJIXUBn8fHLrddqKdUDw9jH2LpLe4fY1sTqBhLsXxRPmJSYwDQaGpCSN3RQKMBNjtvBgD8efNPBL4P5DhR9aNiLMVi02IhZELI8+VhoGbAdRxCCKkxw+yGYbjdcAiZEJ6nPJGRm8F1pGpFxViKFbaKG2g0AJ9HPypCSN2ysf9GNFBvgLCkMPxy+Reu41Qr+g0vxQpvD6ejosNxEkIIqXn1letjl9suAMCmh5twPuw8x4mqDxVjKfYp6xMAQFtZm+MkhBDCjV6WvTC93XQAwDi/cfiU+YnjRNWDirEUK/zSaatQMSaE1F3Ley6HjY4N4tPjMensJDDGuI5U5agYSzFqGRNCCKAsr4x9A/ZBji+H4y+P48CzA1xHqnJUjKWYuGVMxZgQUse1MWqD37v8DgCYem6q+IZItQUVYykmbhnTaWpCCMG8zvPQvkF7pOakYozPGIiYiOtIVYaKsRSj09SEEPIfOb4c9g3YBxV5Ffi/9cfae2u5jlRlqBhLMRrARQghRTXSboRVvVcBAOZdnYcXCS84TlQ1qBhLMWoZE0JIcRNbT0Q/q37IEeZg5KmRyBXmch3pm1ExllKMMWoZE0JICXg8Hna47oC2sjaC4oOw6PoiriN9MyrGUiozLxM5whwA1DImhJCvGaobYut3WwEAK26vwO2o2xwn+jYyX4yXL18OHo+HmTNniqdt27YN3bp1g4aGBng8HpKTk8vdzubNm9G8eXNoaGhAQ0MDHTp0wPnz3N16rfAUtYJAAWoKapzlIIQQaTXIdhBG2Y+CiIkwymcU0nLSuI5UaTJdjB88eICtW7eiefPmRaZnZmaib9+++PXXXyXelrGxMZYvX45Hjx7h4cOH6NGjB9zc3PDiBTeDA768xpjH43GSgRBCpN26vutgqmmKN5/f4KeLP3Edp9Jkthinp6djxIgR2L59O+rXr19k3syZMzF37lw4ODhIvD0XFxf0798fjRo1QuPGjbF06VKoqanh3r17VR1dInSNMSGElE9TSRN73PeABx7+ffIvToec5jpSpchsMZ46dSqcnZ3Rs2fPKt+2UCjE4cOHkZGRgQ4dOpS6XE5ODlJTU4u8qgrdfYsQQiTTzbwbfupQ0Cr+4fQPSMhI4DhRxclkMT58+DAeP34Mb2/vKt3us2fPoKamBkVFRUyaNAmnTp2Cra1tqct7e3tDU1NT/DIxMamyLNQyJoQQyf3Z40/Y6dkhISMBXqe9ZO5hEjJXjKOjozFjxgwcOHAASkpKVbpta2trBAUFITAwEJMnT8bo0aPx8uXLUpefN28eUlJSxK/o6Ogqy0ItY0IIkZySnBL2D9gPeb48fEN8sTtoN9eRKkTmivGjR4+QkJCAVq1aQU5ODnJycrhx4wbWrVsHOTk5CIXCSm9bQUEBVlZWaN26Nby9vWFvb4+1a0u/3ZqioqJ49HXhq6rQDT8IIaRi7A3s8Uf3PwAA0y9MR+TnSI4TSU7mirGTkxOePXuGoKAg8atNmzYYMWIEgoKCIBAIqmxfIpEIOTk5Vba9iqDT1IQQUnE/O/6MTqadkJ6bjlE+oyAUVb6BVpPkuA5QUerq6rCzsysyTVVVFdra2uLp8fHxiI+PR3h4OICCvmB1dXWYmppCS0sLQEFRHzBgAKZNmwag4JRzv379YGpqirS0NBw8eBDXr1/HxYsXa/Do/kOnqQkhpOIEfAH2uu9F8y3NERAVgFV3V+GXjr9wHatcMtcylsSWLVvQsmVLTJgwAQDQpUsXtGzZEn5+fuJlIiIikJiYKH6fkJCAUaNGwdraGk5OTnjw4AEuXryIXr161Xh+gFrGhBBSWRb1LbC2b0EX4/xr8/E0/inHicrHY7I25EyKpaamQlNTEykpKd/cf2y1zgoRnyMQMDYAHU07VlFC6RMXF4dt27bBy8sLhoaGXMchhNQSjDEMODIAviG+GGAzACeHnqyxfVemFtTKlnFtQC1jQgipPB6PhyXdlwAAzoadRXJ2MreBykHFWAqJmEj8xamvVL/shQkhhJSomV4zNNVtilxhLk6+qrmWcWVQMZZCOfn/jeBWVVDlMAkhhMguHo+H4XbDAQCHnh/iOE3ZqBhLoaz8LPH/K8lV7Y1NCCGkLhnerKAYX4u8hri0OI7TlI6KsRTKzs8GAMjx5SDHl7mrzwghRGo0rN8QDsYOEDERjr44ynWcUlExlkJZeQUtY2oVE0LIt/Ow8wAAHHx+kOMkpaNiLIUKW8bKcsocJyGEENk3pOkQ8Hl83I+5j4ikCK7jlIiKsRQq7DOmljEhhHw7fTV9OFk4AZDegVxUjKWQuGUsTy1jQgipCh7NCk5VH3h2QCofr0jFWApRnzEhhFStATYDoChQxOvE13j6Qfpuj0nFWApRnzEhhFQtTSVNfNf4OwDAoWfSd6qairEUoj5jQgipeoWnqg89PwQRE3GcpigqxlKI+owJIaTq9W/UHxqKGohOjcbtqNtcxymCirEUoj5jQgipekpyShjYZCAA4OAz6brmmIqxFKI+Y0IIqR6FNwA59vIY8oR5HKf5DxVjKUR9xoQQUj26W3SHvqo+PmV9wuU3l7mOI0bFWApRy5gQQqqHHF8OQ5sOBSBdp6qpGEsh6jMmhJDqUziq2ue1DzLzMjlOU4CKsRQqbBlTMSaEkKrXrkE7NKzfEBl5GTgdcprrOACoGEulwj5jurSJEEKqHo/Hw3C7guccS8uTnCr8sNycnBwEBgbi3bt3yMzMhK6uLlq2bAkLC4vqyFcnUcuYEEKql0czDyy9tRTnw84jJTsFmkqanOaRuBjfvn0ba9euxenTp5GXlwdNTU0oKysjKSkJOTk5aNiwIby8vDBp0iSoq6tXZ+ZaT9wypgFchBBSLWx1bWFZ3xIRnyMQGBOI3pa9Oc0j0WlqV1dXDB06FObm5rh06RLS0tLw6dMnvH//HpmZmQgLC8P8+fNx9epVNG7cGJcvS89wcVlELWNCCKl+jiaOAIA70Xc4TiJhy9jZ2RknTpyAvLx8ifMbNmyIhg0bYvTo0Xj58iXi4uKqNGRdUziamvqMCSGk+jiaOGJf8D7ZKcYTJ06UeIO2trawtbWtdCBCLWNCCKkJHYw7AADuvb8HoUgIAV/AWRYaTS2FqM+YEEKqn52eHdQU1JCWm4YXH19wmkXiAVwWFhbg8XhlLsPj8RAREfHNoeo6ahkTQkj1E/AFcDB2wJU3V3A3+i6a6zfnLIvExXjmzJmlznv79i22bt2KnJycqshU51GfMSGE1AxHY0dceXMFd97fwcQ2knfJVjWJT1PPmDGj2MvT0xNv377F5s2b0bZtW9y+XTPPh7x58yZcXFxgZGQEHo8HHx+fIvMXLVoEGxsbqKqqon79+ujZsycCAwPL3KZQKMSCBQtgYWEBZWVlWFpa4o8//gBjrBqPpGTUMiaEkJohLSOqK9VnnJWVhaVLl8LS0hL+/v44efIkbty4AQcHh6rOV6KMjAzY29tj48aNJc5v3LgxNmzYgGfPniEgIADm5ubo3bs3Pn78WOo2V6xYgc2bN2PDhg149eoVVqxYgZUrV2L9+vXVdRiloj5jQgipGe2N24MHHsKTwpGQkcBZjgrdgUsoFGL79u1YvHgxlJSUsG7dOowcObLcvuSq1q9fP/Tr16/U+R4eHkXer169Gjt27EBwcDCcnJxKXOfOnTtwc3ODs7MzAMDc3ByHDh3C/fv3qy64hKhlTAghNaOeUj3Y6trixccXuBt9F242bpzkkLhlfPToUTRp0gS///475s6di5CQEHh6etZ4Ia6o3NxcbNu2DZqamrC3ty91OUdHR1y9ehWhoaEAgKdPnyIgIKDMop+Tk4PU1NQir2/FGPvvEYrUZ0wIIdVOGk5VS9wyHjZsGJSVlTF8+HC8e/cOc+fOLXG51atXV1m4b3HmzBkMGzYMmZmZMDQ0xOXLl6Gjo1Pq8nPnzkVqaipsbGwgEAggFAqxdOlSjBgxotR1vL29sXjx4irNnSP8bxActYwJIaT6OZo4Yvvj7bjzXgaKcZcuXcq9dEmaWsndu3dHUFAQEhMTsX37dgwZMgSBgYHQ09MrcfmjR4/iwIEDOHjwIJo2bYqgoCDMnDkTRkZGGD16dInrzJs3Dz/99JP4fWpqKkxMTL4pd+FIaoD6jAkhpCYUtowfxDxArjAXCgKFGs8gcTG+fv16NcaoeqqqqrCysoKVlRUcHBzQqFEj7NixA/PmzStx+dmzZ2Pu3LkYNmwYAKBZs2Z49+4dvL29Sy3GioqKUFRUrNLchaeo+Tw+5PgVfqgWIYSQCmqk1Qjaytr4lPUJQfFBaNegXY1nqPBo6ufPn5c67+tLjKSJSCQq8zrozMxM8PlFPw6BQACRSFTd0Yr4ciS1NJ1pIISQ2orH43Heb1zhYtynTx9ERkYWm37ixIky+1erUnp6OoKCghAUFAQAiIyMRFBQEKKiopCRkYFff/0V9+7dw7t37/Do0SOMGzcOMTExGDx4sHgbTk5O2LBhg/i9i4sLli5dirNnz+Lt27c4deoUVq9ejQEDBtTIMRWikdSEEFLzCu9TzVUxrvB50B9++AE9e/bE7du3YWBgAAA4cuQIxo0bh927d1d1vhI9fPgQ3bt3F78v7LcdPXo0tmzZgtevX2PPnj1ITEyEtrY22rZti1u3bqFp06bidSIiIpCYmCh+v379eixYsABTpkxBQkICjIyMMHHiRPz+++81ckyF6O5bhBBS8wpbxrejb4MxVuNnJitcjBcvXoykpCT07NkTN2/exIULF/DDDz9g3759GDRoUHVkLKZbt25l3hnr5MmT5W7j7du3Rd6rq6tjzZo1WLNmzTem+zbUMiaEkJrXtkFbCHgCxKbFIjo1GqaapjW6/0rdgWv9+vWwt7eHg4MDJkyYgEOHDtVYIa7t6O5bhBBS81TkVdDSsCUAbk5VS9Qy9vPzKzZt4MCBuHXrFoYPHw4ejydextXVtWoT1jHUMiaEEG44GjviYexD3I66jWF2w2p03xIVY3d391Ln7dy5Ezt37gRQMCJNKBRWSbC6ivqMCSGEG3Z6dgCAN8lvanzfEhXjmr68py6jljEhhHCDy3s7VKrPmFQf6jMmhJC6p1J/Bjx48AD+/v5ISEgo1mqWlntTyypqGRNCSN1T4WK8bNkyzJ8/H9bW1tDX1y9yLRbdMerbUZ8xIYTUPRUuxmvXrsXOnTsxZsyYaohDxC1jAbWMCSGkrqhwnzGfz0fHjh2rIwvBF33G1DImhJA6o8LFeNasWdi4cWN1ZCGgPmNCCKmLKnya+ueff4azszMsLS1ha2sLeXn5IvMluRUlKZ24z5hGUxNCSJ1R4WI8ffp0+Pv7o3v37tDW1qZBWyWIj49HRkZGpdY9/fg0DGGIq8+u4ofGP1RxMulT+LCOLx/aQQghXMhNzoUhDKGSrYK4uLhKbyctLa3C6/BYWU9cKIG6ujoOHz4MZ2fnCu+stktNTYWmpibmzp0LJSU6zUwIIXVRdnY2li9fjpSUFGhoaEi0ToVbxlpaWrC0tKxwuLpk7NixUFdXr9S6Y3zG4FnCMwxsMhC/df6tipNJn8TERJw8eRIDBw6Ejo4O13EIIXWY32s/LL65GB1NOmJdv3WV3k5aWhqWL19eoXUqXIwXLVqEhQsXYteuXVBRUano6nWCgYGBxH8Nfa2RWSNcSrgELV0tGBoaVnEy6aWjo1OnjpcQIn0U4hUQhzhkKmV+0+8jVVXVCq9T4WK8bt06REREQF9fH+bm5sUGcD1+/LjCIch/CgduFQ7kIoQQUvtVuBiX9QQn8u0KL2kqvMSJEEJI7VfhYrxw4cLqyEH+X+HNPgpv/kEIIaT2k+imHxUccE2+AbWMCSGk7pGoGDdt2hSHDx9Gbm5umcuFhYVh8uTJFR5FRv4j7jOmljEhhNQZEp2mXr9+PebMmYMpU6agV69eaNOmDYyMjKCkpITPnz/j5cuXCAgIwIsXLzBt2jRMnjy5unPXWtQyJoSQukeiYuzk5ISHDx8iICAAR44cwYEDB/Du3TtkZWVBR0cHLVu2xKhRozBixAjUr1+/ujPXauI+YxpNTQghdUaFBnB16tQJnTp1qq4sBNQyJoQQrnzI+AAAUFNQq/F9V/ipTaR6UZ8xIYRw4+77uwCA9g3a1/i+qRhLGWoZE0JIzWOM4U70HQCAo4ljje+firGUoT5jQgipeeFJ4UjMTISiQBEtDVrW+P6pGEsZahkTQkjNK2wVtzFqA0U5xRrfPxVjKVPYZ0zFmBBCag6Xp6iBShTjHj16YPHixcWmf/78GT169KiSUGU5efIkevfuDW1tbfB4PAQFBRVbZuLEibC0tISysjJ0dXXh5uaG169fl7nd9PR0TJs2DcbGxlBWVoatrS22bNlSTUdRusKWMQ3gIoSQmnPnvYwV4+vXr2PDhg1wd3dHRkaGeHpubi5u3LhRpeFKkpGRgU6dOmHFihWlLtO6dWvs2rULr169wsWLF8EYQ+/evSEUCktd56effsKFCxewf/9+vHr1CjNnzsS0adPg5+dXHYdRqsI+41xhLkRMVKP7JoSQuiglOwUvEl4AADoYd+AkQ6VOU1+5cgXx8fFwcHDA27dvqzhS2Tw9PfH777+jZ8+epS7j5eWFLl26wNzcHK1atcKff/6J6OjoMrPeuXMHo0ePRrdu3WBubg4vLy/Y29vj/v371XAUpStsGQN0qpoQQmpCYEwgGBgs61tCX02fkwyVKsaGhoa4ceMGmjVrhrZt2+L69etVHKvqZGRkYNeuXbCwsICJiUmpyzk6OsLPzw8xMTFgjMHf3x+hoaHo3bt3qevk5OQgNTW1yOtbUTEmhJCaVdhf3MGEm1YxUIlizOPxAACKioo4ePAgZsyYgb59+2LTpk1VHu5bbNq0CWpqalBTU8P58+dx+fJlKCgolLr8+vXrYWtrC2NjYygoKKBv377YuHEjunTpUuo63t7e0NTUFL/KKvaSkuPLQY5fcGM0uryJEEKqn3jwljE3/cVAJYrx149TnD9/Pg4cOIBVq1ZVWahCBw4cEBdUNTU13Lp1S+J1R4wYgSdPnuDGjRto3LgxhgwZguzs0lua69evx7179+Dn54dHjx5h1apVmDp1Kq5cuVLqOvPmzUNKSor4FR0dXaHjKw2NqCaEkJohFAlx7/09ANwN3gIqeG9qAIiMjISOjk6RaYMGDYK1tTUePXpUZcEAwNXVFe3b/3dbsgYNGki8bmFrtVGjRnBwcED9+vVx6tQpDB8+vNiyWVlZ+PXXX3Hq1Ck4OzsDAJo3b46goCD8/fffpfZPKyoqQlGx6q9HU5JTQlpuGo2oJoSQavbi4wuk5aZBTUENdnp2nOWocDE2MzMrcbqdnR3s7Kr2QNTV1aGurv7N22GMgTGGnJycEufn5eUhLy8PfH7REwUCgQAiUc2PaC4cUU0tY0IIqV53owvuR+1g7AABX8BZDomL8bhx4yRabufOnZUOI4mkpCRERUUhNjYWABASEgIAMDAwgIGBAd68eYMjR46gd+/e0NXVxfv377F8+XIoKyujf//+4u3Y2NjA29sbAwYMgIaGBrp27YrZs2dDWVkZZmZmuHHjBvbu3YvVq1dX6/GURHytMfUZE0JItRJfX8xhfzFQgWK8e/dumJmZoWXLlsX6jWuSn58fxo4dK34/bNgwAMDChQuxaNEiKCkp4datW1izZg0+f/4MfX19dOnSBXfu3IGenp54vZCQEKSkpIjfHz58GPPmzcOIESOQlJQEMzMzLF26FJMmTaq5g/t/1GdMCCE1g+s7bxWSuBhPnjwZhw4dQmRkJMaOHYuRI0dCS0urOrOVaMyYMRgzZkyp842MjHDu3Llyt/P1HxQGBgbYtWvXt8arEnQXLkIIqX4JGQkITwoHDzy0N675xyZ+SeLR1Bs3bkRcXBx++eUXnD59GiYmJhgyZIj4Dlek6lCfMSGEVL/C/uKmek1RT6kep1kqdGmToqIihg8fjsuXL+Ply5do2rQppkyZAnNzc6Snp1dXxjqH+owJIaT6iW/2wdEtML9U6ac28fl88Hg8MMbKvOczqTjqMyaEkOp3931By5jr/mKggsU4JycHhw4dQq9evdC4cWM8e/YMGzZsQFRUFNTU1KorY51DfcaEEFK90nLScD+m4NkD0lCMJR7ANWXKFBw+fBgmJiYYN24cDh06VOzmH6RqUMuYEEKql2+IL3KEOWis3RiNtBpxHUfyYrxlyxaYmpqiYcOGuHHjRqmPSzx58mSVhaurqM+YEEKq18FnBwEAHnYe4mcucEniYjxq1CipCFwX0GhqQgipPh8zPuJSxCUAwPBmxW+RzIUK3fSD1AzqMyaEkOpz/OVxCJkQrQ1bo7F2Y67jAPiG0dSk+lCfMSGEVJ+Dz///FHUzD46T/IeKsRSiljEhhFSPd8nvEBAVAB54GNp0KNdxxKgYSyHqMyaEkOpx+PlhAEBX865ooCH5Y3mrGxVjKUSjqQkhpHqIT1HbSc8paoCKsVSiPmNCCKl6LxJeIPhDMOT58hhkO4jrOEVQMZZC1GdMCCFV79DzQwCAfo36QUu55p86WBYqxlKI+owJIaRqMcbEN/oYbicd1xZ/iYqxFKI+Y0IIqVqBMYGITI6EqrwqXBq7cB2nGCrGUoj6jAkhpGoVtordbdyhqqDKcZriqBhLIeozJoSQqpMvysfRF0cBSNeNPr5ExVgKUZ8xIYRUHf9If3zI+ABtZW30atiL6zglomIshajPmBBCqk7htcWDbQdDXiDPcZqSUTGWQoV9xjnCHDDGOE5DCCGyKysvCydengAgvaeoASrGUqmwZQzQqWpCCPkW58LOIS03DSYaJuho2pHrOKWiYiyFCvuMARrERQgh36LwRh/D7IaBz5Pekie9yeowOb6cuHWcmpPKcRpCCJFNKdkpOBN6BoB0n6IGqBhLrcJbtX3K/MRxEkIIkU37gvchR5iDJjpNYK9vz3WcMlExllLaytoAgE9ZVIwJIaSiIj9HYt7VeQCASW0mgcfjcZyobFSMpZSOig4AahkTQkhFCUVCjPIZhfTcdHQ27YypbadyHalcVIyllLYKtYwJIaQyVt1dhYCoAKgpqGGP+x4I+AKuI5VLpovxpEkFpx7WrFlTZPrSpUvh6OgIFRUV1KtXT6JtLVq0CDY2NlBVVUX9+vXRs2dPBAYGVn1oCYlPU1PLmBBCJPY0/inmX5sPAFjXdx0s6ltwnEgyMluMT506hXv37sHIyKjYvNzcXAwePBiTJ0+WeHuNGzfGhg0b8OzZMwQEBMDc3By9e/fGx48fqzK2xKjPmBBCKiY7PxsjT41EnigPbtZuGNNiDNeRJCbHdYDKiImJwY8//oiLFy/C2dm52PzFixcDAHbv3i3xNj08ig57X716NXbs2IHg4GA4OTl9U97KoNPUhBBSMQuuLcDzhOfQU9XDNpdtUj9o60syV4xFIhE8PT0xe/ZsNG3atFr2kZubi23btkFTUxP29qUPh8/JyUFOTo74fWpq1V0TTKepCSFEctffXsequ6sAAP+6/As9VT2OE1WMzJ2mXrFiBeTk5DB9+vQq3/aZM2egpqYGJSUl/PPPP7h8+TJ0dHRKXd7b2xuampril4mJSZVloZYxIYRIJiU7BaN9RoOB4YeWP8DF2oXrSBUm1cX4wIEDUFNTE79u3LiBtWvXYvfu3dVy+qF79+4ICgrCnTt30LdvXwwZMgQJCQmlLj9v3jykpKSIX9HR0VWWhVrGhBAimRkXZiAqJQoN6zfE6j6ruY5TKVJdjF1dXREUFCR+3blzBwkJCTA1NYWcnBzk5OTw7t07/O9//4O5ufk3709VVRVWVlZwcHDAjh07ICcnhx07dpS6vKKiIjQ0NIq8qgq1jAkhpHwnXp7Anqd7wOfxsdd9L9QV1bmOVClS3Wesrq4OdfX/PlgvLy+4uBQ9/dCnTx94enpi7NixVb5/kUhUpE+4JhW2jFNzUpEnzJPaZ3ASQghX4tLiMPHMRADAnI5zpPqpTOWR6mL8NW1tbWhraxeZJi8vDwMDA1hbW4unRUVFISkpCVFRURAKhQgKCgIAWFlZQU1NDQBgY2MDb29vDBgwABkZGVi6dClcXV1haGiIxMREbNy4ETExMRg8eHCNHd+X6inVAw88MDAkZSVBX02fkxyEECKNGGMY7zcen7I+oYVBCyzqtojrSN9EpoqxpH7//Xfs2bNH/L5ly5YAAH9/f3Tr1g0AEBISgpSUFACAQCDA69evsWfPHiQmJkJbWxtt27bFrVu3qm3EdnkEfAHqK9dHUlYSPmV9omJMCCFf2PZoG86Hn4eiQBH7B+yHgkCB60jfROaL8du3b4tN2717d7nXGDPGxP+vpKSEkydPVnGyb6etrF1QjGkQFyGEiIV9CsNPl34CAHg7eaOpHjeNpqok1QO46joaxEUIIUXli/LhecoTmXmZ6G7eHTMcZnAdqUpQMZZidHkTIYQUtTxgOQJjAqGhqIHd7rvB59WOMlY7jqKWopYxIYT852HsQyy+UXC74439N8JU05TjRFWHirEUo5YxIYQUyMrLgucpT+SL8jHYdjBGNBvBdaQqRcVYitGTmwghpMDcK3PxOvE1DNUMsdl5s0w9BEISVIylGJ2mJoQQ4HLEZay7vw4AsNNtp/h3Y21CxViK0WlqQkhd9znrM8b6FtxhcUqbKehr1ZfjRNWDirEUK/zrLzEzkeMkhBDCjannpiImLQaNtBphZa+VXMepNlSMpZi+asFdt+LT4zlOQgghNe/Qs0M49PwQBDwB9g3YB1UFVa4jVRsqxlLMRLPg+cifsz8jLSeN4zSEEFJz3qe+x5RzUwAA87vMR3vj9hwnql5UjKWYhqIG6inVAwBEpURxG4YQQmqIiIkwzncckrOT0caoDX7r/BvXkaodFWMpZ6ZpBgB4l/KO4ySEEFIzNt7fiMtvLkNZThn7B+yvE4+QpWIs5QrvMEMtY0JIXXA/5j5+ufILAOCvXn/BWse6nDVqByrGUk7cMk6mljEhpHY7E3oG3fd0R3Z+NvpY9sGUtlO4jlRjqBhLOXHLOJVaxoSQ2mvLwy1wO+yGzLxM9LHsg2ODj9W6u2yVhYqxlDOrRy1jQkjtJWIizLsyD5PPTi4YuNViHE4PPw11RXWuo9UoOa4DkLJRnzEhpLbKyc/BOL9xOPjsIABgcbfFWNBlQZ1qEReiYizlCvuMY9JikCfMqxOjCgkhtV9ydjIGHBmA62+vQ44vh+0u2zGmxRiuY3GGTlNLOX01fSgIFCBiIsSmxXIdhxBCvllUShQ67uyI62+vQ11BHWc9ztbpQgxQMZZ6fB4fJhoFd+Kia40JIbIuKD4IDv864OXHlzBSN8KtsbfQ27I317E4R8VYBlC/MSGkNrgYfhGdd3VGXHoc7PTscG/8Pdgb2HMdSypQMZYBNKKaECLrdj7ZCeeDzkjPTUd38+64NfaW+P77hIqxTDDVoJYxIUQ2Mcaw6PoijPcbDyETYmTzkbgw8oL4vvukAI2mlgHiljH1GRNCZEieMA8Tz0zErqBdAIBfO/2KP3v8WScvXSoPFWMZUNhnTMWYECIrUnNS8f3R73H5zWUIeAJsct4Er9ZeXMeSWlSMZYCNjg0AIOxTGNJy0urcnWkIIbIlJjUGzged8fTDU6jIq+DY4GPo36g/17GkGvUZywBjDWOY1zOHkAlx9/1druMQQkipnic8R4cdHfD0w1PoqerhxpgbVIglQMVYRnQ27QwAuPXuFsdJCCGkZP6R/ui0sxOiU6NhrW2Ne+PvoY1RG65jyQSZLMavXr2Cq6srNDU1oaqqirZt2yIq6r+RxvHx8fD09ISBgQFUVVXRqlUrnDhxosxtCoVCLFiwABYWFlBWVoalpSX++OMPMMaq+3AkIi7GUVSMCSHS50DwAfTZ3wcpOSnoZNoJd8bfgUV9C65jyQyZ6zOOiIhAp06dMH78eCxevBgaGhp48eIFlJSUxMuMGjUKycnJ8PPzg46ODg4ePIghQ4bg4cOHaNmyZYnbXbFiBTZv3ow9e/agadOmePjwIcaOHQtNTU1Mnz69pg6vVF3MugAA7r2/h5z8HCjKKXKciBBCCi5dWh6wHL9e+xUAMKTpEOxx3wMlOaVy1iRfkrli/Ntvv6F///5YuXKleJqlpWWRZe7cuYPNmzejXbt2AID58+fjn3/+waNHj0otxnfu3IGbmxucnZ0BAObm5jh06BDu379fapacnBzk5OSI36emplb6uMrTWLsx9FT1kJCRgIexD9HRtGO17YsQQiSRL8rHtHPTsPXRVgDA/zr8Dyt7rQSfJ5MnXTklU5+YSCTC2bNn0bhxY/Tp0wd6enpo3749fHx8iizn6OiII0eOICkpCSKRCIcPH0Z2dja6detW6rYdHR1x9epVhIaGAgCePn2KgIAA9OvXr9R1vL29oampKX6ZmFTf3WR4PB46mXYCQKeqCSHcS89Nh/thd2x9tBU88LCu7zr83ftvKsSVJFOfWkJCAtLT07F8+XL07dsXly5dwoABAzBw4EDcuHFDvNzRo0eRl5cHbW1tKCoqYuLEiTh16hSsrKxK3fbcuXMxbNgw2NjYQF5eHi1btsTMmTMxYsSIUteZN28eUlJSxK/o6OgqPd6vdTEtOFV9893Nat0PIYSU5UP6B3Tb3Q1nw85CSU4JJ4acwI/tf+Q6lkyT6tPUBw4cwMSJE8Xvz549CwBwc3PDrFmzAAAtWrTAnTt3sGXLFnTt2hUAsGDBAiQnJ+PKlSvQ0dGBj48PhgwZglu3bqFZs2Yl7uvo0aM4cOAADh48iKZNmyIoKAgzZ86EkZERRo8eXeI6ioqKUFSsub7bzmYFg7huR9+GUCSEgC+osX0TQggAvE58jX4H+uFt8lvoqOjg9PDTcDB24DqWzJPqYuzq6or27duL3+vq6kJOTg62trZFlmvSpAkCAgIAFAzw2rBhA54/f46mTZsCAOzt7XHr1i1s3LgRW7ZsKXFfs2fPFreOAaBZs2Z49+4dvL29Sy3GNc1e3x7qCupIzUnFs4RnaGHQgutIhJA6JCAqAK6HXPE5+zMs61viwsgLsNIq/YwjkZxUF2N1dXWoqxe921Tbtm0REhJSZFpoaCjMzAru35yZmQkA4POLnoEXCAQQiUSl7iszM7PC69Q0AV+AjqYdcSH8Am6+u0nFmBBSY469OAbPU57IEeagfYP2OD38NHRVdbmOVWvIVJ8xUNCCPXLkCLZv347w8HBs2LABp0+fxpQpUwAANjY2sLKywsSJE3H//n1ERERg1apVuHz5Mtzd3cXbcXJywoYNG8TvXVxcsHTpUpw9exZv377FqVOnsHr1agwYMKCmD7FMdL0xIaQmMcaw+u5qDD0+FDnCHLhZu+Ha6GtUiKuYVLeMSzJgwABs2bIF3t7emD59OqytrXHixAl06lQw0lheXh7nzp3D3Llz4eLigvT0dFhZWWHPnj3o3/+/W7JFREQgMTFR/H79+vVYsGABpkyZgoSEBBgZGWHixIn4/fffa/wYy/LlnbgYY/T0E0JItRGKhPjp4k9Yd38dAGBa22lY03cNjVepBjwmLbeYqgVSU1OhqamJlJQUaGhoVMs+svOzobVCC1n5WQgYGyDz1xvHxcVh27Zt8PLygqGhIddxCCH/LysvCyNOjsCp16cAAH/3+hs/dfiJGgASqEwtkLnT1HWdkpwShtsNBwCsDVzLcRpCSG2UmJmIHnt74NTrU1AQKODwoMP4n+P/qBBXIyrGMmiGwwwAwMlXJxGdUr3XNhNC6pbwpHB02NEB997fQ32l+rjieQVD7YZyHavWo2Isg5rrN0c3824QMiE2PtjIdRxCSC0R+D4QHXZ0QHhSOMw0zXB73G3x/Q1I9aJiLKNmtC9oHW97tA2ZeZkcpyGEyDIRE2HNvTXoursrEjMT0cqwFe79cA9NdJtwHa3OoGIso1wau8CingU+Z3/G/uD9XMchhMioqJQo9NzbE7MuzkKOMAcujV1wY8wNGKgZcB2tTqFiLKMEfAGmtZsGAFgXuE5qnrtMCJENjDHsfboXzTY3g/9bf6jIq2Cz82b4DvOFmoIa1/HqHCrGMmxcy3FQlVfFi48vcDXyKtdxCCEy4mPGR3x/7HuM9hmN1JxUOBg7IGhiECa1mUQjpjlCxViG1VOqhzEtxgCgy5wIIZI5E3oGzTY3w8lXJyHHl8PSHktxa+wtNNJuxHW0Oo2KsYz7sV3BY8vOhp5FeFI4x2kIIdIqLScNE/wmwOWQCz5kfEBT3aa4/8N9/Nr5V8jxZe5mjLUOFWMZZ61jjX5W/cDAsD5wPddxCCFSKCAqAPZb7PHvk3/BAw//6/A/PPR6iJaGLbmORv4fFeNaoPAyp11Bu5Cak8pxGkKItMjJz8Gcy3PQZVcXRCZHwkzTDP6j/fF377+hJKfEdTzyBSrGtUBvy96w0bFBWm4adj3ZxXUcQogUCP4QjHb/tsPKOyvBwDCmxRgETw5GV/OuXEcjJaBiXAvweDxMbzcdALDu/jpk5GZwnIgQwhWhSIgVASvQZlsbBH8Ihq6KLk4NPYVdbrugoVg9D7Ah346KcS0xyn4UdFR08ObzG/Q90JdOVxNSB735/AZdd3fF3KtzkSfKg6u1K55PeQ53G3euo5FyUDGuJVQVVHF6+GloKmoiICoAPff2RFJWEtexCCE1gDGG7Y+2o/nm5rgdfRvqCurY6boTPkN9oKeqx3U8IgEqxrWIg7EDro2+Bm1lbTyIfYAee3ogISOB61iEkGoUnx4P18Ou8DrjhYy8DHQx64LgycEY23Is3cBDhlAxrmVaGbbC9THXoa+qj6cfnqLr7q6ITYvlOhYhpBqcfHUSdpvscCb0DBQECvi719/wH+0P83rmXEcjFUTFuBay07PDzbE3YaxhjNeJr9FlVxe8S37HdSxCSBVJyU7BqFOjMOjoIHzK+gR7fXs88nqE/zn+D3we/VqXRfRTq6UaazfGrbG3YFHPAhGfI9B5V2e6QxchtcC1yGtotrkZ9gXvA5/Hx7xO83B/wn3Y6dlxHY18AyrGtZh5PXPcGnsL1trWiE6NRpddXfDy40uuYxFCKiErLwuzLsyC014nRKdGw7K+JW6NvYVlTsugIFDgOh75RlSMa7kGGg1wY8wNNNNrhrj0OHTd3RVB8UFcxyKEVMCj2Edova011gSuAQBMbD0RQZOC4GjiyG0wUmWoGNcB+mr68B/tjzZGbZCYmYjue7oj8H0g17EIIeXIF+Xjjxt/wGGHA14lvoKBmgHOeZzDlu+20DOHaxkqxnWEtoo2rnhegaOJI5Kzk9FzX0/cfHeT61iEkFKEfgpFx50d8fv135Evysdg28F4Pvk5+jXqx3U0Ug2oGNchmkqauDjyInpY9EB6bjr67u+LyxGXuY5FCPkCYwwb729Eiy0tcD/mPuop1cOBgQdw5Psj0FbR5joeqSZUjOsYNQU1nBl+Bv0b9UdWfha+O/QdToec5joWIQRATGoM+uzvg2nnpyErPws9G/bEs8nP4NHMg27gUctRMa6DlOWVcWroKQxsMhC5wlwMPDoQx14c4zoWIXXaoWeHYLfZDpffXIaSnBLW9V2HiyMvwljDmOtopAZQMa6jFAQKOPL9EXg080C+KB/DTgzDvqf7uI5FSJ2TlJWEYceHweOkB5Kzk9HWqC2eTHyCH9v/SDfwqENk7if94cMHjBkzBkZGRlBRUUHfvn0RFhZWbLm7d++iR48eUFVVhYaGBrp06YKsrCyJ9rF8+XLweDzMnDmzitNLFzm+HPa678X4luMhYiKM9hmNbY+2cR2LkDrjQvgF2G2yw5EXRyDgCbCo6yLcHncbNjo2XEcjNUymijFjDO7u7njz5g18fX3x5MkTmJmZoWfPnsjI+O8Zvnfv3kXfvn3Ru3dv3L9/Hw8ePMC0adPA55d/uA8ePMDWrVvRvHnz6jwUqSHgC7DNZRt+bPcjGBgmnpmINffWcB2LkFrtY8ZHTDw9Ef0O9ENcehxsdGxw74d7WNhtIeQF8lzHIxyQ4zpARYSFheHevXt4/vw5mjZtCgDYvHkzDAwMcOjQIfzwww8AgFmzZmH69OmYO3eueF1ra+tyt5+eno4RI0Zg+/bt+PPPP6vnIKQQn8fH2r5roSynjJV3VmLWxVnIzMvEr51/5ToaIbVKVl4W1gauhXeAt/iZ49PbTcfynsuhLK/McTrCJZlqGefk5AAAlJSUxNP4fD4UFRUREBAAAEhISEBgYCD09PTg6OgIfX19dO3aVTy/LFOnToWzszN69uwpcZ7U1NQiL1nF4/GwvOdyLO62GADw27XfMP38dCRnJ3MbjJBaQMRE2B+8H9YbrDHv6jyk5qQWPGFt9HWs7beWCjGRrWJsY2MDU1NTzJs3D58/f0Zubi5WrFiB9+/fIy4uDgDw5s0bAMCiRYswYcIEXLhwAa1atYKTk1OJfcuFDh8+jMePH8Pb21viPN7e3tDU1BS/TExMvu0AOcbj8fB719+xsudKAMD6++thsdYCS28uRXpuOsfpCJFN/pH+aLu9LTxPeSI6NRomGibYN2AfHkx4gK7mXbmOR6SEVBfjAwcOQE1NTfy6d+8eTp48idDQUGhpaUFFRQX+/v7o16+fuD9YJBIBACZOnIixY8eiZcuW+Oeff2BtbY2dO3eWuJ/o6GjMmDEDBw4cKNLqLs+8efOQkpIifkVHR3/7QUuB2R1nw2+YH5rqNkVydjLm+8+HxVoLrL67Gll5kg2CI6Sue/XxFVwPuaLH3h54HPcY6grq8HbyRsi0EIxsPpJGSpMipLrP2NXVFe3btxe/b9CgAZSVlREUFISUlBTk5uZCV1cX7du3R5s2bQAAhoaGAABbW9si22rSpAmioqJK3M+jR4+QkJCAVq1aiacJhULcvHkTGzZsQE5ODgQCQbH1FBUVoaio+M3HKY1crF3Qv1F/HHlxBAuvL0R4Ujj+d+l/WHV3FeZ3no/xrcbTk2IIKcGH9A9YdH0Rtj/eDiETQsATYFKbSVjYdSF0VXW5jkeklFT/aaaurg4rKyvxS1n5v34VTU1N6OrqIiwsDA8fPoSbmxsAwNzcHEZGRggJCSmyrdDQUJiZmZW4HycnJzx79gxBQUHiV5s2bTBixAgEBQWVWIjrAgFfAI9mHng19RV2uO6AqaYpYtNiMeXcFFhvsMbuoN3IF+VzHZMQqZCZl4mlN5fCar0VtjzaAiETwt3GHS+mvMCG/huoEJMySXXLuCTHjh2Drq4uTE1N8ezZM8yYMQPu7u7o3bs3gIJ+z9mzZ2PhwoWwt7dHixYtsGfPHrx+/RrHjx8Xb8fJyQkDBgzAtGnToK6uDju7og/mVlVVhba2drHpdZEcXw7jWo7DiGYjsP3xdiy9tRRvk99irO9YLA8oGPQ1uOlgOu1G6iShSIj9wfvx27XfEJMWAwBoa9QWf/f+G13MunCcjsgKmSvGcXFx+Omnn/DhwwcYGhpi1KhRWLBgQZFlZs6ciezsbMyaNQtJSUmwt7fH5cuXYWlpKV4mIiICiYmJNR1fpinKKWJau2kY13IcNt7fiBW3VyDkUwiGnRiGZQHL8Ef3P+DS2IXuoUvqjCtvrmD25dniZ4SbaZrB28kbQ+2G0h+npEJ4jDHGdYjaIjU1FZqamkhJSYGGhgbXcapdak4q1t5bi7/v/i2+ZrJdg3b4s/uf6Nmwp0RFOS4uDtu2bYOXl5e4v58Qafci4QVmX56N8+HnAQCaipqY32U+prWbBiU5yQeBktqpMrWA/nQjlaahqIEFXRcgckYk5nWaBxV5FdyPuY/e+3uj255uCIgq/9puQmRJfHo8vE57ofmW5jgffh5yfDnMaD8DEdMj8LPjz1SISaVRMSbfTEtZC8ucluHN9DeY2X4mFAWKuPnuJjrv6oy++/viYexDriMS8k0ycjOw5MYSWK2zwvbH2yFiIgxqMggvp7zEmr5r6DnD5JtRMSZVRl9NH//0/QdhP4ZhYuuJkOPL4WLERbTd3hYDjgzAsw/PuI5ISIUIRULsfLITjdY3wsLrC5GRl4H2DdojYGwAjg85jkbajbiOSGoJKsakyplommDLd1vweuprjLIfBT6PD5/XPrDfYg+PEx4I/RTKdURCynUp4hJabm2J8X7jEZceB4t6Fjjy/RHcHX8XHU07ch2P1DJUjEm1sdSyxB73PXg++TkG2w4GA8Oh54dgu9EW433H413yO64jElJM8Idg9NnfB33298GzhGeop1QPq3qvwquprzCk6RC6WoBUCxpNXYXq2mjqigqKD8IC/wU4E3oGACDPl8cUmymo/7I+jaYmnItNi8WCawuwK2gXGBjk+fL4sd2P+K3Lb9BS1uI6HpEhNJqaSLUWBi1wevhp3B1/F04WTsgT5eHoy6MAgDV31yAxk677JjUvPTcdC/0XotH6RtgZtBMMDEOaDsGrqa+wqs8qKsSkRlAxJjXOwdgBV0ZdwbVR19BcvzkAYN+zfbBYa4EF1xbQYxtJjcgX5WP7o+2wWmeFJTeXIDMvE44mjrgz7g6OfH8EllqW5W+EkCpCxZhwprtFd+x0LXiSlo2ODdJz0/HnrT9hsdYCy24to8c2kmrBGMO5sHOw32IPrzNe+JDxAZb1LXF88HEEjA1AB5MOXEckdRAVY8KpwsEw+wfsx4khJ2Cra4vk7GT8du03NFzbEP/c/QfZ+dkcpyS1RVB8EHrt6wXng854+fEltJS1sLbvWryc+hKDbAfR4CzCGSrGRCrweDwMbDIQwZOCsX/AfljWt8THzI/46dJPsFpnhS0PtyBXmMt1TCKj3qe+xxifMWi1tRWuRl6FgkABsx1nI2J6BKa3n06PAyWco2JMpIqAL8CI5iPwauorbHfZDhMNE8SkxWDy2cmw2WCDPUF7IBQJuY5JZERaThrmX5uPxusbY8/TPWBgGG43HCHTQrCy10rUU6rHdURCAFAxJlJKXiCPH1r9gLAfw7Cu7zroq+ojMjkSY3zHwG6zHY6+OAoRE3Edk0ipfFE+tjzcAqv1Vlh6aymy8rPQ2bQzAn8IxMFBB2Fez5zriIQUQcWYSDVFOUX82P5HvJnxBit6roCWshZeJ77G0OND0WprK5wOOQ26VJ4UYozhTOgZNNvcDJPPTkZCRgIaazeGz1Af3BhzA+0atOM6IiElomJMZIKKvAp+6fgLImdEYnG3xdBQ1MDTD0/hetgVHXZ0wJU3V6go12FpOWk49uIYeuztAZdDLnid+Bo6KjrY0G8Dnk9+DjcbNxqcRaSaHNcBCKkIDUUN/N71d0xtOxV/3fkL6wLXITAmEL329UJXs65Y2mMp3Te4johPj4dfiB98Q3xx5c0V8QA/RYEiZjnMwtxOc6GppMlxSkIkQ8WYyCRtFW0s77kcMx1mwvuWN7Y82oIb726g065O6GvVF392/xOtjVpzHZNUsZDEEPi89oFPiA8C3weC4b+zIVZaVhhgMwDT2k2DqaYphykJqTgqxkSmGagZYG2/tfjZ8Wf8efNP7AzaiQvhF3Ah/AIG2AzAku5LYKdnx3VMUkkiJsL9mPsFBfi1D0I+hRSZ365BO7hbu8PNxg1NdJrQqWgis6gYk1rBRNMEW1224peOv2DxjcXYH7wfp16fgs9rHwyyHYTu5t1hr2+P5vrNoa6oznVcUoac/Bxci7wGn9c+8Av1Q3x6vHiePF8ePSx6wM3aDa7Wrmig0YDDpIRUHSrGpFax1LLE3gF7MbfTXCy8vhDHXx4Xv8TL1LeEvYE97PXt0cKgBez17WGqaUqtKg59zvqMc2Hn4Bvii/Ph54vcClVDUQP9G/WHu7U7+lr1pX5gUitRMSa1kq2uLY4NPoYncU9w7OUxPP3wFE/jnyImLQYRnyMQ8TkCJ1+dFC9fT6kemus3Rwv9FuJC3VSvKZTklDg8itotOiUaviG+8A3xxfW315EvyhfPM1I3gpu1G9xt3NHNvBvdIYvUelSMSa3W0rAlWhq2FL9PzEzE0/inBcX5/wv0y48vkZydjJvvbuLmu5viZQU8AWx0bIq1ovXV9Lk4FJnHGMPzhOfwee0D3xBfPIp7VGR+U92m4gLc2qg1+Dy68pLUHVSMSZ2io6IDp4ZOcGroJJ6WK8zFq4+v8PTDUwTFB4mL9KesT3jx8QVefHyBg88OipfXV9UXF+bCQm2tYw05Pv1z+ppQJMTt6NviAvzm8xvxPB54cDRxhLuNO9ys3dBIuxGHSQnhFv32IHWegkChoKga2GOU/SgABa242LTY/4rz/xfo0E+h+JDxARcjLuJixEXxNhQFirDTsxMX6BYGLdBcv3mdvPdxZl4mLkdchk+ID86EnkFiZqJ4nqJAEb0se8Hd2h3fNf6OzjIQ8v+oGBNSAh6PhwYaDdBAowGcGzuLp2fkZuB5wnNxcQ76EITgD8FIz03Ho7hHxU69mmmaFRTnL/qiLepb1LpTsImZiTgTegY+r31wKeISsvKzxPPqK9WHi7UL3Kzd0NuyN9QU1DhMSoh0omJMSAWoKqiivXF7tDduL54mYiJEfo4s1op+l/JO/PIL8RMvr66gjub6zYu0ou307KAir8LFIVXam89v4PvaFz4hPgiICijy4A4zTTPx6efOZp3pFD4h5aB/IYR8Iz6PD0stS1hqWWKQ7SDx9M9ZnxH8IbhIX/SLhBdIy03D7ejbuB19u8g2Gmk1KtYXbaRuJDWXXDHG8DjuMXxDfOHz2gfPEp4Vmd/CoIX4Bhz2+vZSk5sQWUDFmJBqUl+5Prqad0VX867iaXnCPIR+Ci3Sig6KD0JCRgJCPoUg5FMIjrw4Il5eR0WnoDgXjuY2sIeNjk2NXeqTJ8zDjXc3Cm7AEeKH6NRo8TwBT4AuZl3gbuMOV2tXeiwhId9AqorxyZMnsWXLFjx69AhJSUl48uQJWrRoIZ6flJSEhQsX4tKlS4iKioKuri7c3d3xxx9/QFOz4EYAnz59wogRIxAcHIxPnz5BT08Pbm5uWLZsGTQ0NErd9+PHjzFnzhw8ePAAAoEAgwYNwurVq6GmRv1bpOrIC+TRVK8pmuo1xQiMEE+PT48XX3JVWKhDEkOQmJmIq5FXcTXy6n/b4MvDVte2WCtaW0W7SjKm5aThQvgF+Ib44mzYWSRnJ4vnqciroK9VX7hbu8O5sTO0lLWqZJ+E1HVSVYwzMjLQqVMnDBkyBBMmTCg2PzY2FrGxsfj7779ha2uLd+/eYdKkSYiNjcXx4wV3WOLz+XBzc8Off/4JXV1dhIeHY+rUqUhKSsLBgweLbbNwuz179sTQoUOxYcMGpKamYubMmRgzZox4u4RUJwM1AxhYGaCPVR/xtOz8bLxIeFGsLzolJ0X8/kvGGsbFWtFWWlYSDRYr7QlIAKCrogtXa1e427jDycIJyvLKVXfghBAAAI9J4UNg3759CwsLi2It45IcO3YMI0eOREZGBuTkSv7bYt26dfjrr78QHR1d4vxt27ZhwYIFiIuLA59f8Ivr2bNnaN68OcLCwmBlZSVR7tTUVGhqaiIlJaXMVjj5T1xcHLZt2wYvLy8YGhpyHUfqMcbwLuVdsVb0l9fvfklFXuW/wWL/34purt8cagpq5T4Byd3aHe427nAwdoCAL6ipQyRE5lWmFkhVy7gyCg+2tEIcGxuLkydPomvXriXOB4CcnBwoKCiICzEAKCsX/PUfEBBQajHOyclBTk6O+H1qamplDoEQifF4PJjXM4d5PXO42biJp6fmpBYMFvvi7mLPPjxDZl4m7r2/h3vv7/23DfCgq6qLhIyEIttu16Cd+A5Y9AQkQmqWTBfjxMRE/PHHH/Dy8io2b/jw4fD19UVWVhZcXFzw77//lrqdHj164KeffsJff/2FGTNmICMjA3PnzgVQ0HIrjbe3NxYvXvztB0LIN9JQ1EAn007oZNpJPE0oEiIsKazgeugvTnXHpsUiISOBnoBEiDRhHNm/fz9TVVUVv27evCmeFxkZyQCwJ0+elLp+SkoKa9euHevbty/Lzc0tNj8uLo69evWK+fr6MltbWzZ58uQy8xw4cIDp6+szgUDAFBQU2M8//8z09fXZ8uXLS10nOzubpaSkiF/R0dEMAEtJSSn/AyCMMcZiY2PZokWLWGxsLNdR6oyE9AR2J+oOS85K5joKIbVSSkpKhWsBZy1jV1dXtG//340TGjSQ/K/ytLQ09O3bF+rq6jh16hTk5eWLLWNgYAADAwPY2NhAS0sLnTt3xoIFC0rtl/Tw8ICHhwc+fPgAVVVV8Hg8rF69Gg0bNiw1h6KiIhQVFSXOTYg00FXVha6qLtcxCCFf4KwYq6urQ1294g95T01NRZ8+faCoqAg/Pz8oKZX/iDuRqODOQF/275ZGX7/gXrk7d+6EkpISevXqVeGMhBBCSEVIVZ9xUlISoqKiEBsbCwAICQkB8F8rNzU1Fb1790ZmZib279+P1NRU8aApXV1dCAQCnDt3Dh8+fEDbtm2hpqaGFy9eYPbs2ejYsSPMzc0BAPfv38eoUaNw9epVcYt8w4YNcHR0hJqaGi5fvozZs2dj+fLlqFevXo1/DoQQQuoWqSrGfn5+GDt2rPj9sGHDAAALFy7EokWL8PjxYwQGBgJAsRHOkZGRMDc3h7KyMrZv345Zs2YhJycHJiYmGDhwoHhAFgBkZmYiJCQEeXl54mn379/HwoULkZ6eDhsbG2zduhWenp7VebiEEEIIACm9zlhW0XXGFUfXGRNCapvK1ILa9Rw3QgghRAZRMSaEEEI4RsWYEEII4RgVY0IIIYRjVIwJIYQQjlExJoQQQjhGxZgQQgjhGBVjQgghhGNUjAkhhBCOUTEmhBBCOCZV96aWdYV3Fi18eAUpX1paGrKzs5GWlgZVVVWu4xBCyDcrrAEVuds03Zu6Cr1//x4mJiZcxyCEECIFoqOjYWxsLNGyVIyrkEgkQmxsLNTV1cHj8Sq1jdTUVJiYmCA6OloqHzYhzfkoW+VQtsqhbJUnzfmqIhtjDGlpaTAyMgKfL1lvMJ2mrkJ8Pl/iv4LKo6GhIXVf0i9Jcz7KVjmUrXIoW+VJc75vzaapqVmh5WkAFyGEEMIxKsaEEEIIx6gYSxlFRUUsXLgQioqKXEcpkTTno2yVQ9kqh7JVnjTn4yobDeAihBBCOEYtY0IIIYRjVIwJIYQQjlExJoQQQjhGxZgQQgjhGBXjGvLq1Su4urpCU1MTqqqqaNu2LaKiosTzu3XrBh6PV+Q1adKkMreZnp6OadOmwdjYGMrKyrC1tcWWLVskzpSXl4c5c+agWbNmUFVVhZGREUaNGoXY2FjxMm/fvsX48eNhYWEBZWVlWFpaYuHChcjNzS1z2xERERgwYAB0dXWhoaGBIUOG4MOHDxLl8vb2Rtu2baGurg49PT24u7sjJCSkyDLZ2dmYOnUqtLW1oaamhkGDBpW7fcYYfv/9dxgaGkJZWRk9e/ZEWFiYRJm+FBMTg5EjR0JbWxvKyspo1qwZHj58CECyz7QkaWlpmDlzJszMzKCsrAxHR0c8ePCg3Cw3b96Ei4sLjIyMwOPx4OPjU+FjNjc3L/bdW758eZn7jY+Ph6enJwwMDKCqqopWrVrhxIkTFcr2pUmTJoHH42HNmjVFpoeGhsLNzQ06OjrQ0NBAp06d4O/vX2a2r4+l8PXXX39JnO3Dhw8YM2YMjIyMoKKigr59+xb73LZt24Zu3bpBQ0MDPB4PycnJZeYqVNb3B5Ds+z9x4kRYWlpCWVkZurq6cHNzw+vXr0vc36dPn2BsbCxRxvK+C+Vle/v2bamf/7FjxwAAu3fvLnWZhISEUrO5urrC1NQUSkpKMDQ0hKenZ5F/V5s3b0bz5s3FN+vo0KEDzp8/DwBISkrCjz/+CGtraygrK8PU1BTTp09HSkpKkX1ERUXB2dkZKioq0NPTw+zZs5Gfn19qpsr+fiwRI9UuPDycaWlpsdmzZ7PHjx+z8PBw5uvryz58+CBepmvXrmzChAksLi5O/EpJSSlzuxMmTGCWlpbM39+fRUZGsq1btzKBQMB8fX0lypWcnMx69uzJjhw5wl6/fs3u3r3L2rVrx1q3bi1e5vz582zMmDHs4sWLLCIigvn6+jI9PT32v//9r9Ttpqens4YNG7IBAwaw4OBgFhwczNzc3Fjbtm2ZUCgsN1efPn3Yrl272PPnz1lQUBDr378/MzU1Zenp6eJlJk2axExMTNjVq1fZw4cPmYODA3N0dCxzu8uXL2eamprMx8eHPX36lLm6ujILCwuWlZUlwadVICkpiZmZmbExY8awwMBA9ubNG3bx4kUWHh7OGJPsMy3JkCFDmK2tLbtx4wYLCwtjCxcuZBoaGuz9+/dlrnfu3Dn222+/sZMnTzIA7NSpUxU+ZjMzM7ZkyZIi370vP+uS9OrVi7Vt25YFBgayiIgI9scffzA+n88eP34scbZCJ0+eZPb29szIyIj9888/ReY1atSI9e/fnz19+pSFhoayKVOmMBUVFRYXF1dqti+PIy4uju3cuZPxeDwWEREhUTaRSMQcHBxY586d2f3799nr16+Zl5dXse/gP//8w7y9vZm3tzcDwD5//lzmZ8ZY+d8fxiT7/m/dupXduHGDRUZGskePHjEXFxdmYmLC8vPzi+3Tzc2N9evXT6KM5X0XysuWn59f7PNfvHgxU1NTY2lpaYwxxjIzM4st06dPH9a1a9cys61evZrdvXuXvX37lt2+fZt16NCBdejQQTzfz8+PnT17loWGhrKQkBD266+/Mnl5efb8+XP27NkzNnDgQObn58fCw8PZ1atXWaNGjdigQYPE6+fn5zM7OzvWs2dP9uTJE3bu3Dmmo6PD5s2bV2qmyvx+LA0V4xowdOhQNnLkyDKX6dq1K5sxY0aFttu0aVO2ZMmSItNatWrFfvvtt4pGFLt//z4DwN69e1fqMitXrmQWFhalzr948SLj8/lF/phITk5mPB6PXb58ucKZEhISGAB248YN8bbk5eXZsWPHxMu8evWKAWB3794tcRsikYgZGBiwv/76q0gmRUVFdujQIYmzzJkzh3Xq1KlC+cv7TDMzM5lAIGBnzpwpMr2iP8uSiookx2xmZlasCJZHVVWV7d27t8g0LS0ttn37domyFXr//j1r0KABe/78ebEcHz9+ZADYzZs3xdNSU1MZgAp9j9zc3FiPHj1Knf91tpCQEAaAPX/+XDxNKBQyXV3dEo/P399f4mJcme/P19//kjx9+pQBKFLUGWNs06ZNrGvXruzq1asSF+OKfBckydaiRQs2bty4MrchLy9f7PtUHl9fX8bj8Vhubm6py9SvX5/9+++/Jc47evQoU1BQYHl5eYyxgj/Q+Hw+i4+PFy+zefNmpqGhwXJyciTOVd7vx9LQaepqJhKJcPbsWTRu3Bh9+vSBnp4e2rdvX+IpuwMHDkBHRwd2dnaYN28eMjMzy9y2o6Mj/Pz8EBMTA8YY/P39ERoait69e1c6b0pKCng8HurVq1fmMlpaWqXOz8nJAY/HK3LRvJKSEvh8PgICAiqVCYB4n48ePUJeXh569uwpXsbGxgampqa4e/duiduIjIxEfHx8kXU0NTXRvn37UtcpiZ+fH9q0aYPBgwdDT08PLVu2xPbt28vNX9Znmp+fD6FQCCUlpSLTlZWVK/V5FarIMS9fvhza2tpo2bIl/vrrrzJPzQEF370jR44gKSkJIpEIhw8fRnZ2Nrp16yZxPpFIBE9PT8yePRtNmzYtNl9bWxvW1tbYu3cvMjIykJ+fj61bt0JPTw+tW7eWaB8fPnzA2bNnMX78eIlz5eTkAECRnwefz4eiouI3/TyAyn9/AJT6by4jIwO7du2ChYVFkafGvXz5EkuWLMHevXslflgBULHvQnnZHj16hKCgoDI//71790JFRQXff/+9xBmTkpJw4MABODo6Ql5evth8oVCIw4cPIyMjAx06dCg1u4aGBuTkCh7RcPfuXTRr1gz6+vriZfr06YPU1FS8ePFC4mzl/X4sVYXLN6mQuLg4BoCpqKiw1atXsydPnjBvb2/G4/HY9evXxctt3bqVXbhwgQUHB7P9+/ezBg0asAEDBpS57ezsbDZq1CgGgMnJyTEFBQW2Z8+eSmfNyspirVq1Yh4eHqUuExYWxjQ0NNi2bdtKXSYhIYFpaGiwGTNmsIyMDJaens6mTZvGADAvL68KZRIKhczZ2Zl17NhRPO3AgQNMQUGh2LJt27Zlv/zyS4nbuX37NgPAYmNji0wfPHgwGzJkiMR5FBUVmaKiIps3bx57/Pgx27p1K1NSUmK7d+8ucXlJPlPGGOvQoQPr2rUri4mJYfn5+Wzfvn2Mz+ezxo0bS5wNX7XwJD3mVatWMX9/f/b06VO2efNmVq9ePTZr1qwy9/X582fWu3dv8XdPQ0ODXbx4UeJsjDG2bNky1qtXLyYSiRhjJbfKoqOjWevWrRmPx2MCgYAZGhoWORVenhUrVrD69euX2RXxdbbc3FxmamrKBg8ezJKSklhOTg5bvnw5A8B69+5dbP2KtIwr+v0p6ftfaOPGjUxVVZUBYNbW1kVaxdnZ2ax58+Zs3759FcpYke9CWdkKTZ48mTVp0qTMfTZp0oRNnjy5zGUK/fLLL0xFRYUBYA4ODiwxMbHI/ODgYKaqqsoEAgHT1NRkZ8+eLXE7Hz9+ZKampuzXX38VT5swYUKxn29GRgYDwM6dOydRPkl+P5aGinEV279/P1NVVRW/rl+/zgCw4cOHF1nOxcWFDRs2rNTtFJ5W+vq005f++usv1rhxY+bn58eePn3K1q9fz9TU1Eo9hfd1ti9P/+Xm5jIXFxfWsmXLUvuq379/zywtLdn48ePL+ggYYwWnqhs2bCj+JTpy5EjWqlUrNmnSpHLX/dKkSZOYmZkZi46OFk/jshjLy8sX6adijLEff/yROTg4FFtWks+0UHh4OOvSpQsDwAQCAWvbti0bMWIEs7GxkThbZYvx13bs2MHk5ORYdnZ2qctMmzaNtWvXjl25coUFBQWxRYsWMU1NTRYcHCxRtocPHzJ9fX0WExMjnvZ1MRaJRMzV1ZX169ePBQQEsEePHrHJkyezBg0aFDum0lhbW7Np06aVuUxJfyg8fPiQ2dvbi38effr0Yf369WN9+/Yttn5FinFFvj+Mlfz9L5ScnMxCQ0PZjRs3mIuLC2vVqpX4j45Zs2axoUOHVirjl8r6LpSVjbGC7hdNTU32999/l7r9O3fuMADs4cOHEuX5+PEjCwkJYZcuXWIdO3Zk/fv3F/8xxxhjOTk5LCwsjD18+JDNnTuX6ejosBcvXhTZRkpKCmvXrh3r27dvkVPc31qMK/L7sSRUjKtYamoqCwsLE7+Sk5OZnJwc++OPP4os98svv5Q54Cg9PZ0BYBcuXChxfmZmJpOXly/Wzzh+/HjWp08fibJlZmYyxgqKhru7O2vevHmxvzQLxcTEsEaNGjFPT0+JBmEV+vjxo/gXgL6+Plu5cqXE606dOpUZGxuzN2/eFJleWv+XqakpW716dYnbioiIYADYkydPikzv0qULmz59usSZTE1Ni/1j27RpEzMyMioyTZLPtCTp6eniQjNkyBDWv39/idf9uqhU9pifP3/OALDXr1+XOD88PLxYnypjjDk5ObGJEydKlO2ff/4R/6FW+ALA+Hw+MzMzY4wxduXKlWJjDxhjzMrKinl7e5eav9DNmzcZABYUFFTmciUV40LJycksISGBMcZYu3bt2JQpU4otU5FCJ+n3h7HSv/8lycnJYSoqKuzgwYOMMcbs7e0Zn88Xf7Z8Pl/8h8Xvv/9e7vYKlfZdkCTb3r17mby8vPjzK8m4ceNYixYtJM7zpejoaAaA3blzp9RlnJycipyNS01NZR06dGBOTk7FzpYsWLCA2dvbF5n25s0bBqDcszGV/f34JXqecRVTV1eHurp6kWlt27YtdmlCaGgozMzMSt1OUFAQAMDQ0LDE+Xl5ecjLyyvWFyQQCCASiSTOlpeXhyFDhiAsLAz+/v7Q1tYutl5MTAy6d++O1q1bY9euXRXqf9LR0QEAXLt2DQkJCXB1dS13HcYYfvzxR5w6dQrXr1+HhYVFkfmtW7eGvLw8rl69ikGDBgEAQkJCEBUVVWr/kIWFBQwMDHD16lW0aNECQMFDxAMDAzF58mSJj6djx47l/iwl+UxLo6qqClVVVXz+/BkXL17EypUrJV73a5U95qCgIPD5fOjp6ZU4v3AsQ0W+e1/z9PQs0pcNFPTPeXp6YuzYsWXuh8/nS7SfHTt2oHXr1rC3t5coU0kKn0kbFhaGhw8f4o8//qj0tgDJvj/lff9LwgoaVuL+7hMnTiArK0s8/8GDBxg3bhxu3boFS0tLifN+/V2oSLYdO3bA1dUVurq6Jc5PT0/H0aNH4e3tLXGeLxV+BwqPubRlCuenpqaiT58+UFRUhJ+fX7ExGh06dMDSpUuRkJAgPt7Lly9DQ0MDtra2pe7jW34/FlGpEk4q5OTJk0xeXp5t27aNhYWFsfXr1zOBQMBu3brFGCtoaSxZsoQ9fPiQRUZGMl9fX9awYUPWpUuXItuxtrZmJ0+eFL/v2rUra9q0KfP392dv3rxhu3btYkpKSmzTpk0S5crNzWWurq7M2NiYBQUFFbnUoHD04Pv375mVlRVzcnJi79+/L7JMoffv3zNra2sWGBgonrZz50529+5dFh4ezvbt28e0tLTYTz/9JFGuyZMnM01NTXb9+vUi+ytsyTNWcIrM1NSUXbt2jT18+LDYZQ4lfV7Lly9n9erVY76+vuLLrSp6adP9+/eZnJwcW7p0KQsLC2MHDhxgKioqbP/+/RJ/powx1qNHD7Z+/Xrx+wsXLrDz58+zN2/esEuXLjF7e3vWvn37MkeKMsZYWloae/LkCXvy5AkDIB6XUDhyu7xjvnPnDvvnn39YUFAQi4iIYPv372e6urps1KhR4n18/fPNzc1lVlZWrHPnziwwMJCFh4ezv//+m/F4vCJ9dOVl+1pJo6m1tbXZwIEDWVBQEAsJCWE///wzk5eXL9La/frnzFjBqUgVFRW2efPmSn1uR48eZf7+/iwiIoL5+PgwMzMzNnDgwCLbiIuLY0+ePGHbt28Xj/p+8uQJ+/Tpk3iZr3/O5X1/GCv/+x8REcGWLVvGHj58yN69e8du377NXFxcmJaWVpHLJb9UUus9MDCQWVtbiy+fk+S7IMm/TcYK+k55PB47f/58iXkYY+zff/9lSkpKJZ5R+DrbvXv32Pr169mTJ0/Y27dv2dWrV5mjoyOztLQUn0KfO3eu+HKv4OBgNnfuXMbj8dilS5dYSkoKa9++PWvWrBkLDw8vkr3wcrDCS5t69+7NgoKC2IULF5iurm6RS5u+ziXJ70dJUTGuITt27GBWVlZMSUmJ2dvbMx8fH/G8qKgo1qVLF6alpcUUFRWZlZUVmz17drHTcwDYrl27xO/j4uLYmDFjmJGREVNSUmLW1tZs1apVRfpQyhIZGckAlPjy9/dnjDG2a9euUpf5ejuF6zBWcAmHvr4+k5eXZ40aNapQrtL29+WxZ2VlsSlTprD69eszFRUVNmDAgGL/AL5eRyQSsQULFjB9fX2mqKjInJycWEhIiESZvnT69GlmZ2fHFBUVmY2NTZHBGpJ8powVFJ6FCxeK3x85coQ1bNiQKSgoMAMDAzZ16lSWnJxcbpbCX7Jfv0aPHi3RMT969Ii1b9+eaWpqMiUlJdakSRO2bNmyIn2EJf18Q0ND2cCBA5menh5TUVFhzZs3L3ZpSnnZvlbSAK4HDx6w3r17My0tLaaurs4cHByK9d99/XNmrGBApLKycqmfYXnZ1q5dy4yNjZm8vDwzNTVl8+fPL3Z5y8KFC8v9nn79c2as7O9P4fGUtd2YmBjWr18/pqenx+Tl5ZmxsTHz8PAotVvhy+P9svAVTouMjGSMSfZdkOTfJmOMzZs3j5mYmJR5yrZDhw6lDmz8OltwcDDr3r27+Hekubk5mzRpUpHr8MeNG8fMzMyYgoIC09XVZU5OTuzSpUtFtlfSq3AfjDH29u1b1q9fP6asrMx0dHTY//73P/GlTyXlkuT3o6ToEYqEEEIIx+g6Y0IIIYRjVIwJIYQQjlExJoQQQjhGxZgQQgjhGBVjQgghhGNUjAkhhBCOUTEmhBBCOEbFmBBCCOEYFWNCSKXs2LHjm56dDQBv374Fj8cT34v9W8ydOxc//vjjN2+HEC5QMSaklhkzZgx4PB4mTZpUbN7UqVPB4/EwZsyYb9pHdnY2FixYgIULF37TdiRhbm4OHo+He/fuFZk+c+ZMdOvWTfz+559/xp49e/DmzZtqz0RIVaNiTEgtZGJigsOHDxd5ck92djYOHjwIU1PTb97+8ePHoaGhgY4dO37ztiShpKSEOXPmlLmMjo4O+vTpg82bN9dIJkKqEhVjQmqhVq1awcTEBCdPnhRPO3nyJExNTdGyZUvxtL1790JbW7vYY+jc3d3h6elZ6vYPHz4MFxeXItPGjBkDd3d3LFu2DPr6+qhXrx6WLFmC/Px8zJ49G1paWjA2NsauXbtK3a5QKMS4ceNgY2ODqKgo8XQvLy/cu3cP586dK/O4XVxccPjw4TKXIUQaUTEmpJYaN25ckcK3c+dO8bOCCw0ePBhCoRB+fn7iaQkJCTh79izGjRtX6rYDAgLQpk2bYtOvXbuG2NhY3Lx5E6tXr8bChQvx3XffoX79+ggMDMSkSZMwceJEvH//vti6OTk5GDx4MIKCgnDr1q0iLXgLCwtMmjQJ8+bNK/NZxu3atcP79+/x9u3bUpchRBpRMSaklho5ciQCAgLw7t07vHv3Drdv38bIkSOLLKOsrAwPD48iRXv//v0wNTUt0h/7peTkZKSkpMDIyKjYPC0tLaxbtw7W1tYYN24crK2tkZmZiV9//RWNGjXCvHnzoKCggICAgCLrpaenw9nZGR8/foS/v3+JD6SfP38+IiMjceDAgVKPuTDTu3fvSl2GEGlExZiQWkpXVxfOzs7YvXs3du3aBWdnZ+jo6BRbbsKECbh06RJiYmIAALt37xYPAitJYT+0kpJSsXlNmzYFn//frxV9fX00a9ZM/F4gEEBbWxsJCQlF1hs+fDgyMjJw6dIlaGpqlno8P//8M37//Xfk5uaWuIyysjIAIDMzs8T5hEgrKsaE1GLjxo3D7t27sWfPnlJPO7ds2RL29vbYu3cvHj16hBcvXpQ52lpbWxs8Hg+fP38uNk9eXr7Iex6PV+K0r0819+/fH8HBwbh7926Zx/PTTz8hKysLmzZtKnF+UlISAJTYsiZEmlExJqQW69u3L3Jzc5H3f+3av6vxYRzG8evJj+kbf4Eys9nPRClJSharHyUpxWIwCKVsVoNNNhsrKX+AxD9gl6TUGZzh6TmLh8fpcc7dOd6v9f7cd9d2dd/f7+urwuHw1blMJvN+gw6FQvJ4PFdnnU6nfD6f1uv1w3Lm83m1223FYjHNZrOrc5ZlqVarqdVq6XA4XKyvVis5HA75/f6HZQO+AmUM/GA2m02bzUbr9Vo2m+3qXCqV0na7Va/Xu/nj1h/hcPjiu+//KhaLajabikajN8/O5XJyu90aDAYXa/P5XC8vL+/P1cB3QRkDP5zL5ZLL5bo543a7lUgkZFmW4vH4P89Mp9Maj8fa7/cPSvlbqVRSvV5XJBLRYrH464zD4VCj0dDpdLpYGw6HymazD80EfIVf5/P5bDoEAPOCwaD8fr+63e5d88lkUoFAQNVq9ZOT3WcymahcLmu5XMput5uOA3wIN2Pgye12O41GI02nUxUKhbv3dTodWZb1ick+5ng8qt/vU8T4lrgZA0/O6/Vqt9upVqupUqmYjgM8JcoYAADDeKYGAMAwyhgAAMMoYwAADKOMAQAwjDIGAMAwyhgAAMMoYwAADKOMAQAw7A0aq3yOPSvcfAAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "concrete = ConcreteEC2_2004(25)\n", + "reinforcement = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07,) \n", + "# Create section\n", + "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n", + "geo = SurfaceGeometry(poly, concrete)\n", + "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcement, n=3)\n", + "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n", + "sec = GenericSection(geo)\n", + "draw_section(sec)\n", + "\n", + "\n", + "n= 0 *1e3\n", + "res = sec.section_calculator.calculate_mm_interaction_domain(n)\n", + "draw_My_Mz_diagram(res,n,(5,5))\n", + "n= -500 *1e3\n", + "res = sec.section_calculator.calculate_mm_interaction_domain(n)\n", + "draw_My_Mz_diagram(res,n,(5,5))\n", + "#print(res.m_y)\n", + "#print('---------------')\n", + "#print(res.m_z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "10. N-M diagram:\n", + "\n", + "Not working properly. It should be interesting to get the whole diagram and not just the minimum range of M\n", + "(if center of gravity is in the origin it works more or less ok.)\n", + "\n", + "![N-M](N_My.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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OO1FUs7RJkiRJKlr33htsQFKjRjDL5rLIPbK0SZIkSSo6s2fDE08E4xdegKpVw80TAyxtkiRJkopGejp07AiRCFx/PbRqFXaimGBpkyRJklQ0evWCJUvg4IPh8cfDThMzLG2SJEmSCt/rr+/Y1n/UKEhMDDtRzLC0SZIkSSpca9YEyyEBevaEs88ON0+MsbRJkiRJKjyRCNx0E6xeDY0bw8MPh50o5ljaJEmSJBWeCRNg0iQoXRrGjIFy5cJOFHMsbZIkSZIKx/Ll0L17ML7vPjjxxHDzxChLmyRJkqSCF4lA586wYQOcfDKkpISdKGZZ2iRJkiQVvKFDYcaMYDnkmDFQpkzYiWKWpU2SJElSwVq0CO68MxgPHAgNG4abJ8ZZ2iRJkiQVnKws6NABNm+Gc8+FW28NO1HMs7RJkiRJKjiDB8OcOcHFs0eOhHgrx/7yT1CSJElSwfjqq2CXSICnnoL69cPNU0xY2iRJkiTtv4wMaNcOMjOhdWvo2DHsRMWGpU2SJEnS/uvXL5hpq1oVUlMhLi7sRMWGpU2SJEnS/pk7Fx59NBgPHQoHHRRunmLG0iZJkiRp323aBO3bQ3Z2sDzyyivDTlTsWNokSZIk7bvevYPrstWpA0OGhJ2mWLK0SZIkSdo3M2fCM88E4xEjoFKlUOMUV5Y2SZIkSfm3fj106hSMu3eHCy4IN08xZmmTJEmSlH+33QbLl8MRR+zYhESFwtImSZIkKX8mT4axYyE+HsaMgQoVwk5UrFnaJEmSJOXdqlVw443BuE8fOPXUcPOUAJY2SZIkSXkTiUDXrrB2LRx7LDzwQNiJSgRLmyRJkqS8GTUKpk2DsmWDZZFly4adqESwtEmSJEnauyVLoEePYPzgg9CkSahxShJLmyRJkqQ9y84OtvffuBFOPx3uvDPsRCVKzJW2AQMGcPLJJ5OYmEj16tW57LLLWLhwYa5jUlNTOeecc0hKSiIuLo4NGzbs9D4PP/wwp59+OgcccACV8ngRwN9//51bbrmFOnXqUL58eRo3bszQoUML4FtJkiRJUWzIEJg1K9glcswYKFUq7EQlSsyVttmzZ9O9e3c+/vhjZs6cSWZmJs2bN2fTpk05x2zevJmWLVty99137/Z9tm3bxlVXXcVNN92U58/u1asX06dPZ+zYsXz33Xfcfvvt3HLLLUydOnW/vpMkSZIUtb77DlJSgvHgwXDYYeHmKYFKhx0gv6ZPn57r/qhRo6hevTqfffYZzZo1A+D2228HYNasWbt9n379+uW8Pq/mzJlDhw4dOOeccwDo2rUrw4YN49NPP6V169a7fE1GRgYZGRk599PT0/P8eZIkSVKoMjOhfXvYuhVatNix1b+KVMzNtP1VWloaAAceeGChf9bpp5/O1KlTWb58OZFIhPfff5/vv/+e5s2b7/Y1AwYMIDk5OedWt27dQs8pSZIkFYgBA2D+fKhUCYYPh7i4sBOVSDFd2rKzs7n99ts544wzOProowv9855++mkaN25MnTp1KFu2LC1btuSZZ57JmeHblZSUFNLS0nJuy5YtK/SckiRJ0n6bPx/69w/Gzz4LtWuHm6cEi7nlkX/WvXt3vvnmGz766KMi+bynn36ajz/+mKlTp1K/fn0++OADunfvTq1atTj//PN3+ZqEhAQSEhKKJJ8kSZJUILZsCZZFbt8OV10FV18ddqISLWZL2y233MLrr7/OBx98QJ06dQr987Zs2cLdd9/Nq6++SqtWrQBo0qQJCxYsYPDgwbstbZIkSVLMuffeYAOSGjWCWTaXRYYq5kpbJBLh1ltv5dVXX2XWrFkccsghRfK5mZmZZGZmEh+fe0VpqVKlyM7OLpIMkiRJUqGbPRueeCIYv/ACVK0abh7FXmnr3r0748ePZ8qUKSQmJrJq1SoAkpOTKV++PACrVq1i1apVLFq0CICvv/6axMRE6tWrl7NhydKlS1m3bh1Lly4lKyuLBQsWAHD44YdTsWJFABo2bMiAAQO4/PLLSUpK4uyzz+auu+6ifPny1K9fn9mzZzNmzBgef/zxIv5TkCRJkgpBejp07AiRCFx/PfzfCjOFK+ZK23PPPQeQs+3+H0aOHEnHjh0BGDp0aM6W/kDORiF/Pub+++9n9OjROcccf/zxALz//vs5771w4cKc3SkBXnrpJVJSUrjuuutYt24d9evX5+GHH6Zbt24F+RUlSZKkcPTqBUuWwMEHgxMTUSMuEolEwg5RkqSnp5OcnExaWhpJSUlhx5EkSYpZK1euJDU1la5du1KzZs2w48S+11+HSy4Jzl97/304++ywExV7ee0GMb3lvyRJkqQCsGZNsBwSgtk2C1tUsbRJkiRJJVkkAjfdBKtXQ+PG8NBDYSfSX1jaJEmSpJJswgSYNAlKl4YxY6BcubAT6S8sbZIkSVJJtXw5dO8ejO+7D048Mdw82iVLmyRJklQSRSLQuTNs2AAnnwwpKWEn0m5Y2iRJkqSSaOhQmDEjWA45ZgyUKRN2Iu2GpU2SJEkqaRYtgjvvDMYDB0LDhuHm0R5Z2iRJkqSSJCsLOnSAzZvh3HPh1lvDTqS9sLRJkiRJJcngwTBnDiQmwsiREG8liHb+LyRJkiSVFF99FewSCTBkCNSvH24e5YmlTZIkSSoJMjKgXTvIzITWrYMlkooJljZJkiSpJOjXL5hpq1oVUlMhLi7sRMojS5skSZJU3M2dC48+GoyHDYODDgo3j/LF0iZJkiQVZ5s2Qfv2kJ0dLI+84oqwEymfLG2SJElScda7d3Bdtjp1gs1HFHMsbZIkSVJxNWMGPPNMMB4xAipVCjWO9o2lTZIkSSqO1q+Hzp2DcffucMEF4ebRPrO0SZIkScXRbbfB8uVwxBE7NiFRTLK0SZIkScXN5MkwdizEx8OYMVChQtiJtB8sbZIkSVJxsmoV3HhjMO7TB049Ndw82m+WNkmSJKm4iESga1dYuxaOPRYeeCDsRCoAljZJkiSpuBg1CqZNg7Jl4cUXg5+KeZY2SZIkqThYsgR69AjG/fvDMceEGkcFx9ImSZIkxbrsbOjUCTZuhDPOgDvuCDuRCpClTZIkSYp1Q4bArFnBLpGjR0OpUmEnUgGytEmSJEmx7LvvICUlGA8eDIcdFm4eFThLmyRJkhSrMjOhfXvYuhVatNix1b+KFUubJEmSFKseeQTmz4dKlWD4cIiLCzuRCoGlTZIkSYpF8+fDQw8F42efhdq1w82jQmNpkyRJkmLNli3Bssjt2+Gqq+Dqq8NOpEJkaZMkSZJizb33BhuQ1KgRzLK5LLJYs7RJkiRJsWT2bHjiiWD8wgtQtWq4eVToLG2SJElSrEhPh44dIRKB66+HVq3CTqQiYGmTJEmSYkWvXrBkCRx8MDz+eNhpVEQsbZIkSVIseP31Hdv6jx4NiYlhJ1IRsbRJkiRJ0W7NmmA5JASzbc2ahZtHRcrSJkmSJEWzSARuuglWr4bGjXdcm00lhqVNkiRJimYTJsCkSVC6NIwZA+XKhZ1IRczSJkmSJEWr5cuhe/dgfN99cOKJ4eZRKCxtkiRJUjSKRKBzZ9iwAU4+GVJSwk6kkFjaJEmSpGg0dCjMmBEshxwzBsqUCTuRQmJpkyRJkqLNokVw553BeOBAaNgw3DwKlaVNkiRJiiZZWdChA2zeDOeeC7feGnYihczSJkmSJEWTwYNhzpzg4tkjR0K8v7KXdP4/QJIkSYoWX30V7BIJMGQI1K8fbh5FBUubJEmSFA0yMqBdO8jMhNatgyWSEpY2SZIkKTr06xfMtFWtCqmpEBcXdiJFCUubJEmSFLa5c+HRR4PxsGFw0EHh5lFUsbRJkiRJYdq0Cdq3h+zsYHnkFVeEnUhRxtImSZIkhal37+C6bHXqBJuPSH9haZMkSZLCMmMGPPNMMB4xAipVCjWOopOlTZIkSQrD+vXQuXMwvuUWuOCCcPMoalnaJEmSpDDcdhssXw5HHLFjExJpFyxtkiRJUlGbPBnGjoX4eBgzBg44IOxEimKWNkmSJKkorVoFN94YjPv0gVNPDTePop6lTZIkSSoqkQh07Qpr18Kxx8IDD4SdSDHA0iZJkiQVlVGjYNo0KFsWXnwx+CnthaVNkiRJKgpLlkCPHsG4f3845phQ4yh2WNokSZKkwpadDZ06wcaNcMYZcMcdYSdSDLG0SZIkSYVtyBCYNQsqVIDRo6FUqbATKYZY2iRJkqTC9N13kJISjAcPhsMOCzePYo6lTZIkSSosmZnQvj1s3QotWuzY6l/KB0ubJEmSVFgeeQTmz4fKlWH4cIiLCzuRYpClTZIkSSoM8+fDQw8F42eegdq1w82jmGVpkyRJkgrali3Bssjt26FNG7j66rATKYZZ2iRJkqSCdu+9wQYkNWrAs8+6LFL7pUSUtoEDBxIXF8ftt98OwJIlS4iLi9vlbeLEibt9n44dO+50fMuWLYvoW0iSJCkmzJ4NTzwRjF94AapUCTePYl7psAMUtnnz5jFs2DCaNGmS81jdunVZuXJlruNSU1MZNGgQF1544R7fr2XLlowcOTLnfkJCQsEGliRJUuxKT4eOHSESgeuvh1atwk6kYqBYl7bff/+d6667jueff56H/jgJFChVqhQ1atTIdeyrr75KmzZtqFix4h7fMyEhYafXSpIkSQD06gVLlsDBB8Pjj4edRsVEsV4e2b17d1q1asX555+/x+M+++wzFixYQJcuXfb6nrNmzaJ69eo0aNCAm266ibVr1+7x+IyMDNLT03PdJEmSVAy9/vqObf1Hj4bExLATqZgotjNtL730Ep9//jnz5s3b67HDhw+nUaNGnH766Xs8rmXLllxxxRUccsgh/Pjjj9x9991ceOGFzJ07l1KlSu3yNQMGDKBfv3779B0kSZIUI9asCZZDQjDb1qxZuHlUrBTL0rZs2TJ69OjBzJkzKVeu3B6P3bJlC+PHj+e+++7b6/te/aetWo855hiaNGnCYYcdxqxZszjvvPN2+ZqUlBR69eqVcz89PZ26devm8ZtIkiQp6kUicNNNsHo1NG6849psUgEplssjP/vsM3799VdOOOEESpcuTenSpZk9ezZDhgyhdOnSZGVl5Rw7adIkNm/eTPv27fP9OYceeihVq1Zl0aJFuz0mISGBpKSkXDdJkiQVIxMmwKRJULo0jBkDe5k0kPKrWM60nXfeeXz99de5HuvUqRMNGzakd+/euZYyDh8+nNatW1OtWrV8f84vv/zC2rVrqVmz5n5nliRJUgxavhy6dw/G998PJ54Ybh4VS8WytCUmJnL00UfneqxChQpUqVIl1+OLFi3igw8+4M0339zl+zRs2JABAwZw+eWX8/vvv9OvXz+uvPJKatSowY8//sg///lPDj/8cFq0aFGo30eSJElRKBKBzp1hwwY4+WRISQk7kYqpYrk8Mq9GjBhBnTp1aN68+S6fX7hwIWlpaUBwmYCvvvqK1q1bc+SRR9KlSxdOPPFEPvzwQ6/VJkmSVBINHQozZgTLIceMCZZHSoUgLhKJRMIOUZKkp6eTnJxMWlqa57dJkiTth5UrV5KamkrXrl2L/nSVRYvg2GNh82Z48kno0aNoP1/FQl67QYmeaZMkSZLyLSsLOnQICtu558Ktt4adSMWcpU2SJEnKj8GDYc6c4OLZI0dCvL9Sq3D5/zBJkiQpr776Cv64vu+QIVC/frh5VCJY2iRJkqS8yMiAdu0gMxNatw6WSEpFwNImSZIk5UW/fsFMW9WqkJoKcXFhJ1IJYWmTJEmS9mbuXHj00WA8bBgcdFC4eVSiWNokSZKkPdm0Cdq3h+zsYHnkFVeEnUgljKVNkiRJ2pPevYPrstWpE2w+IhUxS5skSZK0OzNmwDPPBOORI6FSpVDjqGSytEmSJEm7sn49dO4cjG+5Bc4/P9w8KrEsbZIkSdKu3HYbLF8ORxyxYxMSKQSWNkmSJOmvJk+GsWMhPh7GjIEDDgg7kUowS5skSZL0Z6tWwY03BuM+feDUU8PNoxLP0iZJkiT9IRKBrl1h7Vo49lh44IGwE0mWNkmSJCnHqFEwbRqULQsvvhj8lEJWOuwAJdWqVavYtGlT2DEkSZJi1po1a3L93G8rVsDDD0PNmsEmJFWrwsqVBfPe0i5s3LgxT8fFRSKRSCFn0Z+kp6eTnJxMnz59KFeuXNhxJEmSJIVk69atDBw4kLS0NJKSknZ7nKWtiP1R2hYuXEhiYmLYcSRJkmLWmjVreOWVV7jiiiuoWrXq/r3Z+PHw2GNQrhy89BLUrVswIaU92LhxIw0aNNhraXN5ZEhq1Kixx/9hJEmSlDdVq1alZs2a+/4G330H994LW7fC0KFwyikFF07agwoVKuTpODcikSRJUsmVmQnt2weFrWXLYOdIKcpY2iRJklRyPfIIzJ8PlSvDCy9AXFzYiaSdWNokSZJUMs2fDw89FIyfeQZq1w43j7QbljZJkiSVPFu2BMsit2+HNm3g6qvDTiTtlqVNkiRJJc+99wYbkNSoAc8+67JIRTVLmyRJkkqW2bPhiSeC8QsvQJUq4eaR9sLSJkmSpJIjPR06doRIBK6/Hlq1CjuRtFeWNkmSJJUcvXrBkiVw8MHw+ONhp5HyxNImSZKkkuH112H48OD8tdGjITEx7ERSnljaJEmSVPytWRMsh4Rgtq1Zs3DzSPlgaZMkSVLxFonATTfB6tXQuPGOa7NJMcLSJkmSpOJtwgSYNAlKl4YXX4Ry5cJOJOWLpU2SJEnF1/Ll0L17ML7/fjjhhHDzSPvA0iZJkqTiKRKBzp1hwwY4+WRISQk7kbRPLG2SJEkqnoYOhRkzguWQY8YEyyOlGGRpkyRJUvGzaBHceWcwHjgQGjYMN4+0HyxtkiRJKl6ysqBDB9i8Gc49F269NexE0n6xtEmSJKl4GTwY5swJLp49ciTE+yuvYpv/D5YkSVLx8dVXcN99wXjIEKhfP9w8UgGwtEmSJKl4yMiAdu0gMxMuvTRYIikVA5Y2SZIkFQ/9+gUzbVWrQmoqxMWFnUgqEJY2SZIkxb65c+HRR4NxaipUrx5uHqkAWdokSZIU27ZsgfbtITs7WB55+eVhJ5IKlKVNkiRJse2pp4LrstWpE2w+IhUzljZJkiTFtokTg58jR0KlSqFGkQqDpU2SJEmxKT19x/iWW+D888PLIhUiS5skSZJi06BBwc+6dXdsQiIVQ5Y2SZIkxZ7Jk+HNN4Nx//5wwAHh5pEKkaVNkiRJsWXVKrjxxh33jzkmvCxSEbC0SZIkKXZEItC1K6xdC0ccEXYaqUhY2iRJkhQ7Ro2CadOgbNlgWaRUAljaJEmSFBuWLIEePYJx//7OtKnEsLRJkiQp+mVnQ6dOsHEjnHEG3HFH2ImkImNpkyRJUvQbMgRmzYIKFWD0aChVKuxEUpGxtEmSJCm6ffcd9OkTjB97DA47LNw8UhGztEmSJCl6ZWZC+/aQkQEtWwY7R0oljKVNkiRJ0euRR2D+fKhcGV54AeLiwk4kFTlLmyRJkqLT/Pnw0EPB+JlnoHbtcPNIIbG0SZIkKfps2RIsi9y+Hdq0gauvDjuRFBpLmyRJkqLPvfcGG5DUqAHPPuuySJVoljZJkiRFl9mz4YkngvELL0CVKuHmkUJmaZMkSVL0SE+Hjh0hEoEbboBWrcJOJIXO0iZJkqTo0asXLFkChxwSXJNNkqVNkiRJUeL112H48OD8tVGjIDEx7ERSVLC0SZIkKXxr1sD11wfjXr2gWbNw80hRxNImSZKkcEUicNNNsHo1NG6849pskgBLmyRJksI2YQJMmgSlS8OLL0K5cmEnkqKKpU2SJEnhWb4cuncPxvffDyecEG4eKQrFXGl75ZVXaN68OVWqVCEuLo4FCxbsdExqairnnHMOSUlJxMXFsWHDhl2+1xtvvEHTpk0pX748lStX5rLLLtvjZ69evZqOHTtSq1YtDjjgAFq2bMkPP/yw/19KkiSpJIpEoHNn2LABTj4ZUlLCTiRFpZgrbZs2beLMM8/k0Ucf3e0xmzdvpmXLltx99927PWby5Mm0a9eOTp068eWXX/Kf//yHa6+9drfHRyIRLrvsMn766SemTJnCF198Qf369Tn//PPZtGnTfn0nSZKkEmnoUJgxI1gOOWZMsDxS0k5i7p+Mdu3aAbBkyZLdHnP77bcDMGvWrF0+v337dnr06MGgQYPo0qVLzuONGzfe7Xv+8MMPfPzxx3zzzTccddRRADz33HPUqFGDCRMmcP0fux1JkiRp7xYtgjvvDMYDB0LDhuHmkaJYzM20FYTPP/+c5cuXEx8fz/HHH0/NmjW58MIL+eabb3b7moyMDADK/enE2Pj4eBISEvjoo4/2+Lr09PRcN0mSpBItKws6dIDNm+Hcc+HWW8NOJEW1ElnafvrpJwD69u3Lvffey+uvv07lypU555xzWLdu3S5f07BhQ+rVq0dKSgrr169n27ZtPProo/zyyy+sXLlyt581YMAAkpOTc25169YtlO8kSZIUMwYPhjlzICkpuIh2fIn8lVTKs6j+J2TcuHFUrFgx5/bhhx8WyPtmZ2cDcM8993DllVdy4oknMnLkSOLi4pg4ceIuX1OmTBleeeUVvv/+ew488EAOOOAA3n//fS688ELi9/AXTUpKCmlpaTm3ZcuWFch3kCRJiklffQX33ReMn3oK6tULN48UA6L6nLbWrVvTtGnTnPu1a9cukPetWbMmkPsctoSEBA499FCWLl2629edeOKJLFiwgLS0NLZt20a1atVo2rQpJ5100m5fk5CQQEJCQoHkliRJimkZGdCuHWRmwqWXBkskJe1VVJe2xMREEhMTC/x9TzzxRBISEli4cCFnnnkmAJmZmSxZsoT69evv9fXJyclAsDnJ/Pnz6d+/f4FnlCRJKnb69Qtm2qpWhdRUiIsLO5EUE6K6tO3KunXrWLp0KStWrABg4cKFANSoUYMaNWoAsGrVKlatWsWiRYsA+Prrr0lMTKRevXoceOCBJCUl0a1bNx544AHq1q1L/fr1GTRoEABXXXVVzmc1bNiQAQMGcPnllwMwceJEqlWrRr169fj666/p0aMHl112Gc2bNy+y7y9JkhST5s6FPy7ZlJoK1auHm0eKITFX2qZOnUqnTp1y7l999dUAPPDAA/Tt2xeAoUOH0q9fv5xjmjVrBsDIkSPp2LEjAIMGDaJ06dK0a9eOLVu20LRpU9577z0qV66c87qFCxeSlpaWc3/lypX06tWL1atXU7NmTdq3b899f6zJliRJ0q5t2gTt20N2drA88v/+g7ikvImLRCKRsEOUJOnp6SQnJ5OWlkZSUlLYcSRJkgpf9+7w7LNQpw58/TVUqlQgb7ty5UpSU1Pp2rVrzp4FUizJazeI6t0jJUmSFONmzAgKG8DIkQVW2KSSxNImSZKkwrF+PXTuHIxvuQXOPz/cPFKMsrRJkiSpcNx2GyxfDkceuWMTEkn5ZmmTJElSwZs8GcaOhfh4GD0aDjgg7ERSzLK0SZIkqWCtWgU33hiMU1Lg1FPDzSPFOEubJEmSCk4kAl27wtq1cNxxcP/9YSeSYp6lTZIkSQVn1CiYNg3KloUxY4KfkvaLpU2SJEkFY8kS6NEjGPfvD8ccE2ocqbiwtEmSJGn/ZWdDp06wcSOccQbccUfYiaRiw9ImSZKk/TdkCMyaBRUqBLtFlioVdiKp2LC0SZIkaf989x306ROMH3sMDjss3DxSMWNpkyRJ0r7LzIT27SEjA1q2DHaOlFSgLG2SJEnad488AvPnQ+XK8MILEBcXdiKp2LG0SZIkad/Mnw8PPRSMn30WatcON49UTFnaJEmSlH9btgTLIrdvhzZt4Oqrw04kFVuWNkmSJOXfvfcGG5DUqBHMskkqNJY2SZIk5c/s2fDEE8F4+HCoUiXcPFIxZ2mTJElS3qWnQ8eOEInADTfARReFnUgq9ixtkiRJyrtevWDJEjjkkOCabJIKnaVNkiRJefP668FyyLg4GDUKEhPDTiSVCJY2SZIk7d2aNXD99cG4Vy9o1izcPFIJYmmTJEnSnkUicNNNsHo1NG6849pskoqEpU2SJEl7NmECTJoEpUvDiy9CuXJhJ5JKFEubJEmSdm/5cujePRjffz+ccEK4eaQSyNImSZKkXYtEoHNn2LABTjkFUlLCTiSVSJY2SZIk7drQoTBjRrAccvToYHmkpCJnaZMkSdLOFi2CO+8Mxo8+Cg0bhptHKsEsbZIkScotKws6dIDNm+Hcc+GWW8JOJJVoljZJkiTlNngwzJkDSUnBRbTj/ZVRCpP/BEqSJGmHr76C++4Lxk89BfXqhZtHkqVNkiRJ/ycjA9q1g8xMuPTSYImkpNBZ2iRJkhTo1y+YaataFVJTIS4u7ESSsLRJkiQJYO7cYJdICApb9erh5pGUw9ImSZJU0m3aBO3bQ3Z2sDzy8svDTiTpTyxtkiRJJd0//xlcl61OHRgyJOw0kv7C0iZJklSSzZgBzz4bjEeOhEqVQo0jaWeWNkmSpJJq/Xro3DkY33ILnH9+uHkk7ZKlTZIkqaS69VZYvhyOOGLHJiSSoo6lTZIkqSSaOBHGjYNSpeDFF+GAA8JOJGk3LG2SJEklzcqV0K1bML77bmjaNNw8kvbI0iZJklSSRCLQpQusWwcnnAD33Rd2Ikl7YWmTJEkqSZ5/Ht56CxISgmWRZcqEnUjSXljaJEmSSooff4RevYLxgAHQuHG4eSTliaVNkiSpJMjKgvbtYdMmOOcc6NEj7ESS8sjSJkmSVBIMHgxz5kBiIowaBfH+GijFCv9plSRJKu6+/HLHhiNDhkD9+uHmkZQvljZJkqTiLCMD2rWDzEy47DLo0CHsRJLyydImSZJUnN1/P3z9NVSvDsOGQVxc2Ikk5ZOlTZIkqbj66CMYNCgYp6YGxU1SzLG0SZIkFUcbNwa7RUYi0KkTXHpp2Ikk7SNLmyRJUnF0xx2weHGw6ciTT4adRtJ+sLRJkiQVN2+8Ac8/H5y/Nno0JCWFnUjSfrC0SZIkFSdr1kCXLsG4Z084++xw80jab5Y2SZKk4iISgW7dYPVqaNwYHn447ESSCoClTZIkqbgYNw4mT4bSpWHsWChXLuxEkgqApU2SJKk4WLYMbrklGPftC8cfH2ocSQXH0iZJkhTrsrODbf3T0uDUU6F377ATSSpAljZJkqRY98wz8O67cMABMGZMsDxSUrFhaZMkSYpl//sf/POfwXjQIDjiiHDzSCpwljZJkqRYlZkJ7drB1q3QogXcdFPYiSQVAkubJElSrBowAObPh0qVYPjw4GLakoodS5skSVIsmj8fHnwwGD/7LNSuHW4eSYXG0iZJkhRrtmwJlkVmZcE//gHXXBN2IkmFyNImSZIUa1JSgg1IatYMZtkkFWuWNkmSpFjy3nvw1FPBeMQIOPDAcPNIKnSWNkmSpFixYQN07BiMu3WDli3DTCOpiFjaJEmSYkWPHrBsGRx2GAweHHYaSUUkpkpbZmYmvXv35phjjqFChQrUqlWL9u3bs2LFilzHff/991x66aVUrVqVpKQkzjzzTN5///1cx9x2222ceOKJJCQkcNxxx+31s9etW8ett95KgwYNKF++PPXq1eO2224jLS2tIL+iJEnSrr3yCowZA/Hx8OKLUKFC2IkkFZGYKm2bN2/m888/57777uPzzz/nlVdeYeHChbRu3TrXcRdffDHbt2/nvffe47PPPuPYY4/l4osvZtWqVbmO69y5M//4xz/y9NkrVqxgxYoVDB48mG+++YZRo0Yxffp0unTpUmDfT5IkaZdWrYIbbwzGffrAaaeFm0dSkYqLRCKRsEPsj3nz5nHKKafw888/U69ePdasWUO1atX44IMPOOusswDYuHEjSUlJzJw5k/PPPz/X6/v27ctrr73GggUL8v3ZEydOpG3btmzatInSpUvn6TXp6ekkJyeTlpZGUlJSvj9TkiSVMJEIXHopTJsGxx4Ln34KZcuGnSoqrFy5ktTUVLp27UrNmjXDjiPlW167QUzNtO1KWloacXFxVKpUCYAqVarQoEEDxowZw6ZNm9i+fTvDhg2jevXqnHjiiQX+2UlJSXssbBkZGaSnp+e6SZIk5dmIEUFhK1sWxo61sEklUN6mh6LU1q1b6d27N9dcc01OM42Li+Odd97hsssuIzExkfj4eKpXr8706dOpXLlygX32mjVr6N+/P127dt3jcQMGDKBfv34F9rmSJKkEWbwYbr89GD/8MBx9dKhxJIUjqmfaxo0bR8WKFXNuH374Yc5zmZmZtGnThkgkwnPPPZfzeCQSoXv37lSvXp0PP/yQTz/9lMsuu4xLLrmElStXFkiu9PR0WrVqRePGjenbt+8ej01JSSEtLS3ntmzZsgLJIEmSirmsLOjQAX7/Hc46C3r2DDuRpJBE9Uxb69atadq0ac792rVrAzsK288//8x7772Xa/3ne++9x+uvv8769etzHn/22WeZOXMmo0ePpk+fPvuVaePGjbRs2ZLExEReffVVypQps8fjExISSEhI2K/PlCRJJdATT8CHH0LFijB6NJQqFXYiSSGJ6tKWmJhIYmJirsf+KGw//PAD77//PlWqVMn1/ObNmwGIj889iRgfH092dvZ+5UlPT6dFixYkJCQwdepUypUrt1/vJ0mStEtffw333BOMn3wSDjkk1DiSwhXVyyP/KjMzk7///e/Mnz+fcePGkZWVxapVq1i1ahXbtm0D4LTTTqNy5cp06NCBL7/8ku+//5677rqLxYsX06pVq5z3WrRoEQsWLGDVqlVs2bKFBQsWsGDBgpz3Wb58OQ0bNuTTTz8FgsLWvHlzNm3axPDhw0lPT8/57KysrKL/w5AkScXTtm3Qrl3w85JLoHPnsBNJCllUz7T91fLly5k6dSrAThfEfv/99znnnHOoWrUq06dP55577uFvf/sbmZmZHHXUUUyZMoVjjz025/jrr7+e2bNn59w//vjjAVi8eDEHH3wwmZmZLFy4MGfm7vPPP+eTTz4B4PDDD8/12X+8RpIkab/16wdffglVq8Lzz0NcXNiJJIUspkrbwQcfTF4uK3fSSSfx9ttv7/GYWbNm5euzzjnnnDx9tiRJ0j6bMwcGDgzGw4bBQQeFm0dSVNin0rZ06VJ+/vlnNm/eTLVq1TjqqKPcbEOSJGl//P47tG8P2dnBzyuuCDuRpCiR59K2ZMkSnnvuOV566SV++eWXXLNOZcuW5ayzzqJr165ceeWVO20CIkmSpL246y748UeoWxeGDAk7jaQokqd2ddttt3HssceyePFiHnroIb799lvS0tLYtm0bq1at4s033+TMM8/k/vvvp0mTJsybN6+wc0uSJBUfb70FQ4cG41GjIDk51DiSokueZtoqVKjATz/9tNP2+gDVq1fnb3/7G3/729944IEHmD59OsuWLePkk08u8LCSJEnFztq10KVLMO7RA/72t3DzSIo6eSptAwYMyPMbtmzZcp/DSJIklTjdu8PKldCwIeTjdy5JJYcnn0mSJIXlpZfg5ZehdGl48UUoXz7sRJKiUJ43Ijn33HOJ28t1QuLi4nj33Xf3O5QkSVKxt3w53HRTML7vPjjppHDzSIpaeS5tf72Y9Z9t3LiR8ePHk5GRURCZJEmSirdIBDp3hg0b4OSTISUl7ESSolieS9sTTzyx02Pbt2/nmWee4eGHH6Z27dr079+/QMNJkiQVS889BzNmQLlyMGYMlCkTdiJJUWyfLq4NMG7cOO6//362bNlC37596dq1K6VL7/PbSZIklQw//AB33hmM/9//CzYgkaQ9yHfLmj59On369GHx4sXceeed9OrViwoVKhRGNkmSpOJl+3Zo3x62bIHzzgt2jpSkvchzafv000/p3bs3H3/8Md26deOdd96hatWqhZlNkiSpeBk4ED7+OLh49siREO9G3pL2Ls+l7dRTT6V8+fJ069aNQw45hPHjx+/yuNtuu63AwkmSJBUbn30G/foF42eegbp1w80jKWbkubTVq1ePuLg4Xnvttd0eExcXZ2mTJEn6qy1boG3bYHlkmzZw7bVhJ5IUQ/Jc2pYsWVKIMSRJkoqxPn3gf/+DmjWDnSP3cu1bSfqzfC+k/uWXX3b73Mcff7xfYSRJkoqdd96BIUOC8ciRcOCB4eaRFHPyXdqaN2/OunXrdnr8P//5Dy1btiyQUJIkScXC+vXQsWMwvvlmaNEi1DiSYlO+S9upp55K8+bN2bhxY85jH3zwARdddBEPPPBAgYaTJEmKad27w/LlcOSRwTXZJGkf5Lu0vfDCC9SrV49LLrmEjIwM3n//fVq1asWDDz5Iz549CyOjJElS7HnpJZgwAUqVghdfBK9rK2kf5bu0xcfH89JLL1GmTBn+9re/0bp1awYMGECPHj0KI58kSVLsWb4cbropGN97L5xySrh5JMW0PO0e+dVXX+30WN++fbnmmmto27YtzZo1yzmmSZMmBZtQkiQplmRnQ6dOsGEDnHwy3HNP2Ikkxbg8lbbjjjuOuLg4IpFIzmN/3B82bBipqalEIhHi4uLIysoqtLCSJElR79lnYeZMKF8+WBZZpkzYiSTFuDyVtsWLFxd2DkmSpNj3v//BXXcF40GDoEGDcPNIKhbyVNrq169f2DkkSZJiW2YmtGsHW7cGW/vffHPYiSQVE3kqbX+1YsUKPvroI3799Veys7NzPXfbbbcVSDBJkqSY8tBDMH8+VK4MI0ZAXFzYiSQVE/kubaNGjeLGG2+kbNmyVKlShbg//YUUFxdnaZMkSSXPJ5/Aww8H46FDoVatcPNIKlbyXdruu+8+7r//flJSUoiPz/cVAyRJkoqXTZuCZZFZWXDdddCmTdiJJBUz+W5dmzdv5uqrr7awSZIkQbDxyA8/QJ068K9/hZ1GUjGU7+bVpUsXJk6cWBhZJEmSYstbb8FzzwXjUaOgUqUw00gqpvK9PHLAgAFcfPHFTJ8+nWOOOYYyf7n2yOOPP15g4SRJkqLW2rXQuXMw7tEDzjsv3DySiq19Km1vv/02Df7vuiN/3YhEkiSp2ItEoFs3WLUKGjWCAQPCTiSpGMt3aXvssccYMWIEHTt2LIQ4kiRJMWDcOJg0CUqXhrFjoXz5sBNJKsbyfU5bQkICZ5xxRmFkkSRJin5Ll0L37sG4b1844YRQ40gq/vJd2nr06MHTTz9dGFkkSZKiW3Y2dOwI6elw2mnQu3fYiSSVAPleHvnpp5/y3nvv8frrr3PUUUfttBHJK6+8UmDhJEmSospTT8H770OFCjBmTLA8UpIKWb7/pqlUqRJXXHFFYWSRJEmKXt98Aykpwfjxx+Hww8PNI6nEyHdpGzlyZGHkkCRJil7btkHbtpCRAa1awQ03hJ1IUgmS73PaJEmSSpy+feHLL6FKFXjhBfAyR5KKUJ5KW8uWLfn444/3etzGjRt59NFHeeaZZ/Y7mCRJUlT46CN49NFgnJoKNWqEm0dSiZOn5ZFXXXUVV155JcnJyVxyySWcdNJJ1KpVi3LlyrF+/Xq+/fZbPvroI958801atWrFoEGDCju3JElS4du4Edq337FrpOf1SwpBnkpbly5daNu2LRMnTuTll18mNTWVtLQ0AOLi4mjcuDEtWrRg3rx5NGrUqFADS5IkFZlevWDxYqhfP9g5UpJCkOeNSBISEmjbti1t27YFIC0tjS1btlClSpWdtv2XJEmKeVOn7jh/bfRoSEoKO5GkEmqfLy6SnJxMcnJyQWaRJEmKDr/+umOHyDvvhLPPDjePpBLN3SMlSZL+LBKBrl2D4nbMMdC/f9iJJJVwljZJkqQ/GzkSpkyBsmVh7FhISAg7kaQSztImSZL0h8WLoUePYNy/PzRpEm4eScLSJkmSFMjKCrb3//13OOssuOOOsBNJErAPpa1Dhw588MEHhZFFkiQpPIMHBxfSTkyEMWOgVKmwE0kSsA+lLS0tjfPPP58jjjiCRx55hOXLlxdGLkmSpKLz5Zdw333B+Kmn4OCDQ40jSX+W79L22muvsXz5cm666SZefvllDj74YC688EImTZpEZmZmYWSUJEkqPFu3wnXXQWYmXHYZdOwYdiJJymWfzmmrVq0avXr14ssvv+STTz7h8MMPp127dtSqVYuePXvyww8/FHROSZKkwpGSAv/9Lxx0EKSmBhfTlqQosl8bkaxcuZKZM2cyc+ZMSpUqxUUXXcTXX39N48aNeeKJJwoqoyRJUuGYOROefDIYjxgB1aqFGkeSdiXfpS0zM5PJkydz8cUXU79+fSZOnMjtt9/OihUrGD16NO+88w7//ve/efDBBwsjryRJUsFYt27HUsibb4aLLgo1jiTtTun8vqBmzZpkZ2dzzTXX8Omnn3LcccftdMy5555LpUqVCiCeJElSIYhEoFs3WLECGjSAQYPCTiRJu5Xv0vbEE09w1VVXUa5cud0eU6lSJRYvXrxfwSRJkgrN2LEwcSKULh2MDzgg7ESStFv5Lm3t2rUrjBySJElFY8kS6N49GPftCyedFGYaSdqrPJe2zp075+m4ESNG7HMYSZKkQpWVBe3bw8aNcMYZ0KdP2Ikkaa/yXNpGjRpF/fr1Of7444lEIoWZSZIkqXAMGgQffgiJifDii1CqVNiJJGmv8lzabrrpJiZMmMDixYvp1KkTbdu25cADDyzMbJIkSQXn88/h/vuD8ZAhcMgh4eaRpDzK85b/zzzzDCtXruSf//wn06ZNo27durRp04a3337bmTdJkhTdNm+G666DzEy48kro0CHsRJKUZ/m6TltCQgLXXHMNM2fO5Ntvv+Woo47i5ptv5uCDD+b3338vrIySJEn7p3dv+N//oGZNGDYM4uLCTiRJeZbvi2vnvDA+nri4OCKRCFlZWQWZSZIkqeBMnw7/+lcwHjkSqlQJN48k5VO+SltGRgYTJkzgggsu4Mgjj+Trr7/mX//6F0uXLqVixYqFlVGSJGnfrFkDnToF41tvhRYtws0jSfsgzxuR3Hzzzbz00kvUrVuXzp07M2HCBKpWrVqY2SRJkvZdJAI33girVkGjRvDoo2EnkqR9kufSNnToUOrVq8ehhx7K7NmzmT179i6Pe+WVVwosnCRJ0j4bNQpeeQXKlIFx46B8+bATSdI+yXNpa9++PXGetCtJkmLBTz/BbbcF4/794fjjw80jSfshXxfXliRJinrbt0O7dvD779CsGdx5Z9iJJGm/7PPukdGgW7duxMXF8eSTT+7y+YyMDI477jji4uJYsGBBzuNLliwhLi5up9vHH3+8188cNWoUTZo0oVy5clSvXp3u3bsX0LeRJEkFYuBAmDMHkpJgzBgoVSrsRJK0X/I80xZtXn31VT7++GNq1aq122P++c9/UqtWLb788stdPv/OO+9w1FFH5dyvspctgB9//HEee+wxBg0aRNOmTdm0aRNLlizZp/ySJKkQzJsH/foF42eegfr1w80jSQUgJkvb8uXLufXWW3n77bdp1arVLo956623mDFjBpMnT+att97a5TFVqlShRo0aefrM9evXc++99zJt2jTOO++8nMebNGmS/y8gSZIK3qZN0LZtsDyyTRu47rqwE0lSgYi55ZHZ2dm0a9eOu+66K9cs2Z+tXr2aG264gRdffJEDDjhgt+/VunVrqlevzplnnsnUqVP3+LkzZ84kOzub5cuX06hRI+rUqUObNm1YtmzZHl+XkZFBenp6rpskSSoEd90F338PtWvDc8+BG6hJKiZirrQ9+uijlC5dmtv+2BHqLyKRCB07dqRbt26cdNJJuzymYsWKPPbYY0ycOJE33niDM888k8suu2yPxe2nn34iOzubRx55hCeffJJJkyaxbt06LrjgArZt27bb1w0YMIDk5OScW926dfP3hSVJ0t698UZQ1ABGj4YDDww3jyQVoKheHjlu3DhuvPHGnPtvvPEGTz31FJ9//vluLz/w9NNPs3HjRlJSUnb7vlWrVqVXr145908++WRWrFjBoEGDaN269S5fk52dTWZmJkOGDKF58+YATJgwgRo1avD+++/TokWLXb4uJSUl12elp6db3CRJKki//gqdOwfjnj3hT6cxSFJxENUzba1bt2bBggU5tzlz5vDrr79Sr149SpcuTenSpfn555+54447OPjggwF47733mDt3LgkJCZQuXZrDDz8cgJNOOokOHTrs9rOaNm3KokWLdvt8zZo1AWjcuHHOY9WqVaNq1aosXbp0t69LSEggKSkp102SJBWQSARuuCEobkcfDY88EnYiSSpwUT3TlpiYSGJiYs79rl27cskll+Q6pkWLFrRr145OnToBMGTIEB566KGc51esWEGLFi14+eWXadq06W4/a8GCBTnFbFfOOOMMABYuXEidOnUAWLduHWvWrKG+O1NJkhSO4cNh6lQoWxbGjoVy5cJOJEkFLqpL219VqVJlp235y5QpQ40aNWjQoAEA9erVy/V8xYoVATjssMNyytbo0aMpW7Ysxx9/PACvvPIKI0aM4IUXXsh53auvvkpKSgr/+9//ADjyyCO59NJL6dGjB6mpqSQlJZGSkkLDhg0599xzC+cLS5Kk3fvhB+jRIxg/8ggce2y4eSSpkMRUaStI/fv35+eff6Z06dI0bNiQl19+mb///e85z6elpbFw4cJcrxkzZgw9e/akVatWxMfHc/bZZzN9+nTKlClT1PElSSrZtm+Hdu1g82Y499zgXDZJKqbiIpFIJOwQJUl6ejrJycmkpaV5fpskSfuqXz/o2xeSk+Hrr8FNvkqklStXkpqaSteuXfd4mosUrfLaDaJ6IxJJkqSdfPIJ9O8fjJ97zsImqdiztEmSpNjx++/Qti1kZcG118I114SdSJIKnaVNkiTFjl69YNGiYHbtmWfCTiNJRcLSJkmSYsPUqfD88xAXB6NHQ6VKYSeSpCJhaZMkSdFv9Wq4/vpgfMcdwY6RklRCWNokSVJ0i0SgSxf47Tdo0gQeeijsRJJUpCxtkiQpug0bBm+8AQkJMG5c8FOSShBLmyRJil4LFwabjwAMHAhHHx1uHkkKgaVNkiRFp8zMYHv/LVvg/PPhttvCTiRJobC0SZKk6PTggzB/PlSuDKNGQby/tkgqmfzbT5IkRZ85c+CRR4LxsGFQu3a4eSQpRJY2SZIUXTZuhHbtIDs7+HnVVWEnkqRQWdokSVJ06dEDfvoJ6teHp58OO40khc7SJkmSosfEiTByZHD+2osvQnJy2IkkKXSWNkmSFB2WLYOuXYNxSgqcdVa4eSQpSljaJElS+LKyoH172LABTj4ZHngg7ESSFDUsbZIkKXyPPQazZkGFCjBuHJQpE3YiSYoaljZJkhSuzz6De+8NxkOGwBFHhJtHkqKMpU2SJIVn0ya47jrIzIQrr4ROncJOJElRx9ImSZLCc8cdsHBhcPHs1FSIiws7kSRFHUubJEkKx5QpMGxYUNTGjIEDDww7kSRFJUubJEkqeitXQpcuwfjOO+Fvfws3jyRFMUubJEkqWtnZ0LEjrF0Lxx8P/fuHnUiSopqlTZIkFa0hQ2DGDChfPtjePyEh7ESSFNUsbZIkqeh89RX07h2MH38cGjUKN48kxQBLmyRJKhpbtsC118K2bXDJJXDjjWEnkqSYYGmTJElFo3dv+O9/4aCDYPhwt/eXpDyytEmSpML35pvw9NPBeNQoqFYt1DiSFEssbZIkqXD9+it06hSMe/SAli3DzSNJMcbSJkmSCk8kAp07B8Xt6KNh4MCwE0lSzLG0SZKkwvPss/DGG8G2/uPHQ7lyYSeSpJhjaZMkSYXjv/+FO+8Mxv/v/8Exx4SbR5JilKVNkiQVvIyMYHv/rVuDc9huvTXsRJIUsyxtkiSp4N19d3Ah7apVYeRIt/eXpP1gaZMkSQVr5kx4/PFgPHIk1KgRbh5JinGWNkmSVHB++w06dAjGN98MF18cbh5JKgYsbZIkqWBEItClC6xcCY0awaBBYSeSpGLB0iZJkgrGs8/CtGlQtixMmAAHHBB2IkkqFixtkiRp/339NdxxRzAeNAiOPTbcPJJUjFjaJEnS/tmyBa65Jtjm/6KL3N5fkgqYpU2SJO2fO+8MLqR90EFu7y9JhcDSJkmS9t2UKcG5bABjxkD16uHmkaRiyNImSZL2zfLl0LlzML7jDmjePNw8klRMWdokSVL+ZWVBu3awbh2ccAI88kjYiSSp2LK0SZKk/Bs0CN5/P9jWf8KEYJt/SVKhsLRJkqT8+fRTuO++YPz003DkkeHmkaRiztImSZLybuNGuPZa2L4d2rSBTp3CTiRJxZ6lTZIk5V337vDjj1CvHgwb5vb+klQELG2SJClvxo2DF1+E+HgYPx4qVQo7kSSVCJY2SZK0dz/9BDfdFIzvvx/OOCPcPJJUgljaJEnSnmVmBuexbdwYlLV77gk7kSSVKJY2SZK0Z/36wSefQHJysESydOmwE0lSiWJpkyRJuzdr1o4LZ6emQv36ocaRpJLI0iZJknZt3Tpo2xYiEejcOdjiX5JU5CxtkiRpZ5EIXH89LF8eXDz7qafCTiRJJZalTZIk7Sw1FV59FcqUgQkToGLFsBNJUollaZMkSbl9+y307BmMBwyAE04IN48klXCWNkmStMPWrXDNNbBlCzRvvqO8SZJCY2mTJEk79OkDX30F1arB6NEQ768KkhQ2/yaWJEmBN9/cseHIyJFQo0a4eSRJgKVNkiRBsEtk+/bB+LbboFWrcPNIknJY2iRJKumysuC662DtWjjuOHj00bATSZL+xNImSVJJ178/zJ4NFSrAyy9DuXJhJ5Ik/YmlTZKkkmzWrKC0AQwbFlxIW5IUVSxtkiSVVL/9BtdeC9nZ0KlTsERSkhR1LG2SJJVE2dnQoQOsXAmNGsHTT4edSJK0G5Y2SZJKosceg7feCs5fe/nl4Hw2SVJUisnS9t1339G6dWuSk5OpUKECJ598MkuXLs15fuvWrXTv3p0qVapQsWJFrrzySlavXp3rPd59911OP/10EhMTqVGjBr1792b79u17/Nwff/yRyy+/nGrVqpGUlESbNm12el9JkqLexx/D3XcH4yefhGOOCTWOJGnPYq60/fjjj5x55pk0bNiQWbNm8dVXX3HfffdR7k87XfXs2ZNp06YxceJEZs+ezYoVK7jiiitynv/yyy+56KKLaNmyJV988QUvv/wyU6dOpU+fPrv93E2bNtG8eXPi4uJ47733+M9//sO2bdu45JJLyM7OLtTvLElSgdmwAa65BrZvhzZtoGvXsBNJkvYiLhKJRMIOkR9XX301ZcqU4cUXX9zl82lpaVSrVo3x48fz97//HYD//e9/NGrUiLlz53Lqqady9913M3PmTObNm5fzumnTptGmTRt+/fVXEhMTd3rfGTNmcOGFF7J+/XqSkpJyPqty5crMmDGD888/f5d5MjIyyMjIyLmfnp5O3bp1SUtLy3kfSZKKRCQCV10FkyfDoYfC559DcnLYqaR9tnLlSlJTU+natSs1a9YMO46Ub+np6SQnJ++1G8TUTFt2djZvvPEGRx55JC1atKB69eo0bdqU1157LeeYzz77jMzMzFwlqmHDhtSrV4+5c+cCQZEq95dr0JQvX56tW7fy2Wef7fKzMzIyiIuLIyEhIeexcuXKER8fz0cffbTbzAMGDCA5OTnnVrdu3X356pIk7b+hQ4PCVqYMvPSShU2SYkRMlbZff/2V33//nYEDB9KyZUtmzJjB5ZdfzhVXXMHs2bMBWLVqFWXLlqVSpUq5XnvQQQexatUqAFq0aMGcOXOYMGECWVlZLF++nAcffBAI/ovNrpx66qlUqFCB3r17s3nzZjZt2sSdd95JVlbWbl8DkJKSQlpaWs5t2bJlBfAnIUlSPn35JfTsGYwHDoSTTw43jyQpz6K6tI0bN46KFSvm3BYuXAjApZdeSs+ePTnuuOPo06cPF198MUOHDs3z+zZv3pxBgwbRrVs3EhISOPLII7nooosAiI/f9R9JtWrVmDhxItOmTaNixYokJyezYcMGTjjhhN2+BiAhIYGkpKRcN0mSitTvv8M//gEZGdCq1Y7yJkmKCaXDDrAnrVu3pmnTpjn3q1WrRunSpWncuHGu4xo1apSzRLFGjRps27aNDRs25JptW716NTVq1Mi536tXL3r27MnKlSupXLkyS5YsISUlhUMPPXS3eZo3b86PP/7ImjVrKF26NJUqVaJGjRp7fI0kSaHr3h0WLoTatWHUKIiLCzuRJCkforq0JSYm7rQpyMknn5wz4/aH77//nvr16wNw4oknUqZMGd59912uvPJKABYuXMjSpUs57bTTcr0uLi6OWrVqATBhwgTq1q3LCSecsNdcVatWBeC9997j119/pXXr1vv2BSVJKmxjxgS3+HiYMAH+799hkqTYEdWlbVfuuusu/vGPf9CsWTPOPfdcpk+fzrRp05g1axYAycnJdOnShV69enHggQeSlJTErbfeymmnncapp56a8z6DBg2iZcuWxMfH88orrzBw4ED+/e9/U6pUKQCWL1/Oeeedx5gxYzjllFMAGDlyJI0aNaJatWrMnTuXHj160LNnTxo0aFDkfw6SJO3VwoVw883BuG9fOOusUONIkvZNzJW2yy+/nKFDhzJgwABuu+02GjRowOTJkznzzDNzjnniiSeIj4/nyiuvJCMjgxYtWvDss8/mep+33nqLhx9+mIyMDI499limTJnChRdemPN8ZmYmCxcuZPPmzTmPLVy4kJSUFNatW8fBBx/MPffcQ0/PC5AkRaOtW4PrsG3aBH/7246LaUuSYk7MXact1uX1WgySJO2X7t3h2WehWrVg50ivYaViyOu0KdYVy+u0SZKkPJg8OShsAC++aGGTpBhnaZMkqThZvBi6dAnGvXtDixbh5pEk7TdLmyRJxUVmJlxzDaSlwWmnQf/+YSeSJBUAS5skScXFPffAJ59ApUrB9v5lyoSdSJJUACxtkiQVB2+9BYMGBePhw+H/rl8qSYp9ljZJkmLdihXQvn0wvuUWuOKKcPNIkgqUpU2SpFiWlQVt28KaNXDccTtm2yRJxYalTZKkWHb//fD++1ChArz8MpQrF3YiSVIBs7RJkhSrXn4ZHnkkGD//PBx5ZLh5JEmFwtImSVIsWrAAOnUKxnfdFWz1L0kqlixtkiTFml9/hUsvhS1boGVLGDAg7ESSpEJkaZMkKZZs2wZ//zssXQpHHBFcj61UqbBTSZIKkaVNkqRY0qMHfPghJCXB1KnBhbQlScWapU2SpFgxdGhwi4uD8eOhYcOwE0mSioClTZKkWPDhh3DrrcH4kUegVatw80iSioylTZKkaPfzz3DllbB9O1x9NfTuHXYiSVIRsrRJkhTNNm+Gyy6D336D44+H4cOD5ZGSpBLD0iZJUrSKRKBz5+CabNWqwWuvwQEHhJ1KklTELG2SJEWrgQPh5ZehTBmYPBnq1Qs7kSQpBJY2SZKi0euvwz33BON//QvOOivcPJKk0FjaJEmKNt99B9deGyyPvOkm6No17ESSpBBZ2iRJiibr18Oll8LGjdCsGTz5ZNiJJEkhs7RJkhQtsrLgmmvghx+C89cmTYKyZcNOJUkKmaVNkqRo0acPvP12sEPklCnBjpGSpBLP0iZJUjQYORIGDw7Go0bBcceFmUaSFEUsbZIkhe2993ZsNnLvvXDVVeHmkSRFFUubJElh+u47uOIK2L49OJ/twQfDTiRJijKWNkmSwvLrr9CqFaSlwRlnwIgREBcXdipJUpSxtEmSFIYtW4Kt/RcvhsMOg9deg3Llwk4lSYpCljZJkopadjZ06AAffwyVK8Mbb0DVqmGnkiRFKUubJElF7Z57YOJEKFMGXn0VGjQIO5EkKYpZ2iRJKkrDh8PAgcH4hRfg7LPDzSNJinqWNkmSiso770C3bsH4/vuhfftw80iSYoKlTZKkovDtt/D3vwdb+197LfTtG3YiSVKMsLRJklTYVq/esbX/mWe6tb8kKV8sbZIkFabNm6F1a1iyBA4/PNh4JCEh7FSSpBhiaZMkqbBkZwfnrX36KRx4ILz5plv7S5LyzdImSVJhuftumDx5x9b+RxwRdiJJUgyytEmSVBiefx4efTQYjxgBzZqFm0eSFLMsbZIkFbSZM+Gmm4Jx377Qtm2ocSRJsc3SJklSQfrvf4Ot/bOygrJ2//1hJ5IkxThLmyRJBWXVqmBr//T0YDnkCy+4tb8kab9Z2iRJKgh/bO3/88/BhiOvvOLW/pKkAmFpkyRpf2VnQ7t2MG8eVKkSbO1fpUrYqSRJxYSlTZKk/dWnTzCzVrYsvPZacBFtSZIKiKVNkqT9MWwYDBoUjEeOhDPPDDePJKnYsbRJkrSvJk2Cm28Oxg8+CNdeG24eSVKxZGmTJGlfzJwZlLTsbLjhBrj33rATSZKKKUubJEn59fHHcPnlkJkJV10Fzz3n1v6SpEJjaZMkKT+++QYuugg2bYLmzWHsWChVKuxUkqRizNImSVJeLV4cFLX16+HUU3fsGClJUiGytEmSlBerVsEFF8DKlXD00fDGG1ChQtipJEklgKVNkqS9Wb8eWrSAH3+EQw6BGTPgwAPDTiVJKiEsbZIk7cmmTXDxxfDVV1CjRrBrZM2aYaeSJJUgljZJknZn2zb4+99hzhyoVCmYYTvssLBTSZJKGEubJEm7kpUF7dvD9OlwwAHBOWzHHBN2KklSCWRpkyTpryIRuOUWePllKFMm2CXy9NPDTiVJKqEsbZIk/dV998HQocEFs8eODTYhkSQpJJY2SZL+7PHH4eGHg/HQodCmTbh5JEklnqVNkqQ/jBwJd9wRjAcMgK5dw80jSRKWNkmSAq++CtdfH4zvvBN69w43jyRJ/8fSJknSu+/C1VdDdjZ07gz/7/8F57NJkhQFLG2SpJJt3jy47LLgmmxXXAHDhlnYJElRxdImSSq5vvsOLrwQfv8dzjsPxo+H0qXDTiVJUi6WNklSyfTzz3DBBbB2LZxySnBOW0JC2KkkSdqJpU2SVPKsXh0UtuXLoXFjePNNSEwMO5UkSbsUc6UtLi5ul7dBgwYBMGvWrN0eM2/evJ3eb9GiRSQmJlKpUqU9fu7atWtp2bIltWrVIiEhgbp163LLLbeQnp5eGF9TklRY0tKgZUv44QeoXx9mzIAqVcJOJUnSbsVcaVu5cmWu24gRI4iLi+PKK68E4PTTT9/pmOuvv55DDjmEk046Kdd7ZWZmcs0113DWWWft9XPj4+O59NJLmTp1Kt9//z2jRo3inXfeoVu3boXyPSVJhWDzZrjkEliwAKpXh5kzoXbtsFNJkrRHMXe2dY0aNXLdnzJlCueeey6HHnooAGXLls11TGZmJlOmTOHWW28l7i+7gd177700bNiQ8847jzlz5uzxcytXrsxNN92Uc79+/frcfPPNOTN8kqQol5kJbdrAhx9CcjK8/TYccUTYqSRJ2quYK21/tnr1at544w1Gjx6922OmTp3K2rVr6dSpU67H33vvPSZOnMiCBQt45ZVX8v3ZK1as4JVXXuHss8/e43EZGRlkZGTk3Hc5pSSFIDsbOnaEN96A8uXh9dfhuOPCTiVJUp7E3PLIPxs9ejSJiYlcccUVuz1m+PDhtGjRgjp16uQ8tnbtWjp27MioUaNISkrK12dec801HHDAAdSuXZukpCReeOGFPR4/YMAAkpOTc25169bN1+dJkvZTJAI9euzYzn/SJDjzzLBTSZKUZ1Fd2saNG0fFihVzbh9++GGu50eMGMF1111HuXLldvn6X375hbfffpsuXbrkevyGG27g2muvpVmzZvnO9MQTT/D5558zZcoUfvzxR3r16rXH41NSUkhLS8u5LVu2LN+fKUnaD/36wb/+FVwwe8wYuOiisBNJkpQvcZFIJBJ2iN3ZuHEjq1evzrlfu3ZtypcvD8CHH35Is2bNWLBgAccee+wuX9+/f3+efvppli9fTpkyZXIer1SpEr///nvO/UgkQnZ2NqVKlSI1NZXOnTvnKd9HH33EWWedxYoVK6hZs2aeXpOenk5ycjJpaWn5nuWTJOXTkCHBLBvAs8/Cn85NlhT7Vq5cSWpqKl27ds3z72JSNMlrN4jqc9oSExNJ3M11c4YPH86JJ56428IWiUQYOXIk7du3z1XYAObOnUtWVlbO/SlTpvDoo48yZ84caudjF7Hs7GyAXOesSZKixIsv7ihs/ftb2CRJMSuqS9vupKenM3HiRB577LHdHvPee++xePFirr/++p2ea9SoUa778+fPJz4+nqOPPjrnsVdffZWUlBT+97//AfDmm2+yevVqTj75ZCpWrMh///tf7rrrLs444wwOPvjggvlikqSCMX48/LEB1e23wz33hBpHkqT9EZOl7aWXXiISiXDNNdfs9pjhw4dz+umn07Bhw336jLS0NBYuXJhzv3z58jz//PP07NmTjIwM6tatyxVXXEGfPn326f0lSYUkNRW6dQs2IOnUCR57LDifTZKkGBXV57QVR57TJkmF6LHH4M47g/HNN8PTT0N8VO+5JWk/eE6bYl1eu4H/JpMkxb5IBPr23VHYevcOdoy0sEmSioGYXB4pSVKOSCQoa48/Htx/+GG4++5wM0mSVIAsbZKk2JWVFewK+fzzwf2nnoLbbgs3kyRJBczSJkmKTZmZ0LFjsFNkfDy88MKOHSMlSSpGLG2SpNizdStcfTVMmQKlSwfF7aqrwk4lSVKhsLRJkmLLpk1w2WXwzjtQrhxMngwXXRR2KkmSCo2lTZIUOzZsgFatYM4cqFgRpk2Dc84JO5UkSYXK0iZJig2//QYtWsAXX0ClSjB9OjRtGnYqSZIKnaVNkhT9li+HCy6A776D6tVh5kxo0iTsVJIkFQlLmyQpui1eDOedF/ysUyc4l61Bg7BTSZJUZOLDDiBJ0m599x2ceWZQ2A47DD780MImSSpxLG2SpOj0xRfQrBmsWAFHHRUUtoMPDjuVJElFztImSYo+c+fCuefCmjVw4okwaxbUrBl2KkmSQmFpkyRFl3ffDTYdSUsLlka++y5UrRp2KkmSQmNpkyRFj2nTguuwbdoEzZsH2/onJ4edSpKkUFnaJEnR4aWX4IorICMDLr8cpk6FChXCTiVJUugsbZKk8L3wAlx7LWzfDm3bwr//DQkJYaeSJCkqWNokSeF68km44QaIRKBbNxg9Gkp7GVFJkv5gaZMkhSMSgQcfhJ49g/t33gnPPgvx/qtJkqQ/8z9lSpKKXiQC//wnDB4c3H/wQbj3XoiLCzeXJElRyNImSSpa2dlw880wbFhw/4kn4PbbQ40kSVI0s7RJkorO9u3QsSOMGxfMqqWmwvXXh51KkqSoZmmTJBWNjAy4+mp47bVgo5EXXwzuS5KkPbK0SZIK36ZNwbXXZs4MtvKfOBEuuSTsVJIkxQRLmySpcKWlwcUXw0cfBRfLnjIFzjsv7FSSJMUMS5skqfCsWQMtW8Jnn0FyMrz1Fpx2WtipJEmKKZY2SVLhWLkSzj8fvv0WqlWDGTPguOPCTiVJUsyxtEmSCt6SJUFh+/FHqF0b3nkHGjYMO5UkSTHJ0iZJKlgLFwaF7Zdf4JBD4N13g5+SJGmfxIcdQJJUjHz5JZx1VlDYGjWCDz+0sEmStJ8sbZKkgvHxx3DOOfDbb3D88TB7drA0UpIk7RdLmyRp/733XrAkcsMGOP304H61amGnkiSpWLC0SZL2zxtvwEUXBRfQPu+8YJfISpXCTiVJUrFhaZMk7bt//xsuuwwyMqB1a3j99eAC2pIkqcBY2iRJ+2bECLjmGti+Pfg5aRKUKxd2KkmSih1LmyQpf7Kz4dFHoUuXYHzDDfDii1CmTNjJJEkqlrxOmyQp7375BTp2DK69BtCrFwweDHFxocaSJKk4c6ZNkpQ3//43HHNMUNjKl4fnnrOwSZJUBJxpkyTtWVoa3HprsAQS4KSTYOxYaNAg3FySJJUQzrRJknbvww/h2GODwhYfD/feC3PmWNgkSSpCzrRJkna2bRs88ECw4UgkAoccEsyunX562MkkSSpxLG2SpNy+/RbatoUvvgjud+oETz0FiYnh5pIkqYRyeaQkKZCdDUOGwIknBoWtShWYPDm4HpuFTZKk0DjTJkmCpUuDGbX33gvut2gBI0dCzZrh5pIkSc60SVKJFonAmDHBVv7vvRds5f+vf8Fbb1nYJEmKEs60SVJJ9dtvcOON8Oqrwf1TTw0K3BFHhJtLkiTl4kybJJVEU6bAUUcFha1MGXjkkWB7fwubJElRx5k2SSpJ0tLg9tth1Kjg/tFHB9dgO+64EENJkqQ9caZNkkqK99+HJk2CwhYXB717w/z5FjZJkqKcM22SVNxt3gwpKcF2/gCHHgqjR8OZZ4abS5Ik5YmlTZKKs48+CrbyX7QouH/jjTB4MFSsGG4uSZKUZy6PlKTiaPNm6NkTmjULClvt2vDmmzB0qIVNkqQY40ybJBU3//lPMLv2ww/B/U6d4PHHoVKlUGNJkqR940ybJBUXW7bAHXfAWWcFha12bXjjDRgxwsImSVIMc6ZNkoqDOXOCGbXvvw/uO7smSVKx4UybJMWyLVvgzjuDnSC//x5q1XJ2TZKkYsaZNkmKVXPnBjNqCxcG9zt2hCeesKxJklTMONMmSbFm06ZgZ8gzzggKW61a8PrrMHKkhU2SpGLImTZJiiXvvAM33ABLlgT3O3QIZtcqVw41liRJKjzOtElSLFi/Hjp3hgsuCApbvXowfTqMGmVhkySpmLO0SVK0mzwZGjUKlj/GxcFtt8F//wstWoSdTJIkFQGXR0pStFq5Erp3h1dfDe43agTDh8Npp4WbS5IkFSln2iQp2kQi8MILQUl79VUoXRruuw+++MLCJklSCeRMmyRFkx9/hK5d4b33gvsnnxwUuCZNws0lSZJC40ybJEWD7dvhscfgmGOCwla+fHB/7lwLmyRJJZwzbZIUtk8/hRtvhAULgvt/+xukpsJhh4UaS5IkRQdn2iQpLGlpwUYjp54aFLbKlYOlkO+8Y2GTJEk5Yq60rV69mo4dO1KrVi0OOOAAWrZsyQ8//JDrmBtvvJHDDjuM8uXLU61aNS699FL+97//5Tpm3rx5nHfeeVSqVInKlSvTokULvvzyy71+/ty5c/nb3/5GhQoVSEpKolmzZmzZsqVAv6OkYi4SgZdfhoYN4dlng/vt2sH//gddugTb+kuSJP2fmCptkUiEyy67jJ9++okpU6bwxRdfUL9+fc4//3w2bdqUc9yJJ57IyJEj+e6773j77beJRCI0b96crKwsAH7//XdatmxJvXr1+OSTT/joo49ITEykRYsWZGZm7vbz586dS8uWLWnevDmffvop8+bN45ZbbiE+Pqb+GCWF6aef4KKL4OqrYdUqOPJIePddGDMGqlcPO50kSYpCcZFIJBJ2iLz6/vvvadCgAd988w1HHXUUANnZ2dSoUYNHHnmE66+/fpev++qrrzj22GNZtGgRhx12GPPnz+fkk09m6dKl1K1bF4Cvv/6aJk2a8MMPP3D44Yfv8n1OPfVULrjgAvr377/P3yE9PZ3k5GTS0tJISkra5/eRFGO2bYPBg6F/f9i6FcqWhbvvhj59ICEh7HSSFJNWrlxJamoqXbt2pWbNmmHHkfItr90gpqaIMjIyAChXrlzOY/Hx8SQkJPDRRx/t8jWbNm1i5MiRHHLIITkFrUGDBlSpUoXhw4ezbds2tmzZwvDhw2nUqBEHH3zwLt/n119/5ZNPPqF69eqcfvrpHHTQQZx99tm7/dw/Z05PT891k1TCfPghHH883HNPUNjOOw++/hoeeMDCJkmS9iqmSlvDhg2pV68eKSkprF+/nm3btvHoo4/yyy+/sHLlylzHPvvss1SsWJGKFSvy1ltvMXPmTMqWLQtAYmIis2bNYuzYsZQvX56KFSsyffp03nrrLUqX3vWGmj/99BMAffv25YYbbmD69OmccMIJnHfeeTudU/dnAwYMIDk5Oef2R3GUVAKsXRuco9asGXz7bbD8cexYmDkzWBYpSZKUB1Fd2saNG5dTvCpWrMjHH3/MK6+8wvfff8+BBx7IAQccwPvvv8+FF16403ll1113HV988QWzZ8/myCOPpE2bNmzduhWALVu20KVLF8444ww+/vhj/vOf/3D00UfTqlWr3W4qkp2dDQSbnHTq1Injjz+eJ554ggYNGjBixIjdfoeUlBTS0tJybsuWLSugPx1JUSs7G0aPDjYa+ePvh65dg41GrrvOjUYkSVK+RPV12lq3bk3Tpk1z7teuXZvy5cuzYMEC0tLS2LZtG9WqVaNp06acdNJJuV77x8zWEUccwamnnkrlypV59dVXueaaaxg/fjxLlixh7ty5OWVv/PjxVK5cmSlTpnD11VfvlOWPddKNGzfO9XijRo1YunTpbr9DQkICCS5/kkqOzz+HW24JLooNcPTRMGwYnH56uLkkSVLMiurSlpiYSGJi4i6fS05OBuCHH35g/vz5e9wcJBKJEIlEcs6J27x5M/Hx8cT96b92/3H/jxm1vzr44IOpVasWCxcuzPX4999/z4UXXpiv7yWpGFq7NjhnLTU12MK/QgW4/37o2RPKlAk7nSRJimFRvTxyVyZOnMisWbNytv2/4IILuOyyy2jevDkQnHs2YMAAPvvsM5YuXcqcOXO46qqrKF++PBdddBEAF1xwAevXr6d79+589913/Pe//6VTp06ULl2ac889F4Dly5fTsGFDPv30UwDi4uK46667GDJkCJMmTWLRokXcd999/O9//6NLly7h/GFICl9WFjz3HBxxRDCjFonAtdfCwoXwz39a2CRJ0n6L6pm2XVm5ciW9evVi9erV1KxZk/bt23PfffflPF+uXDk+/PBDnnzySdavX89BBx1Es2bNmDNnDtX/7xpIDRs2ZNq0afTr14/TTjuN+Ph4jj/+eKZPn56zDDIzM5OFCxeyefPmnPe+/fbb2bp1Kz179mTdunUce+yxzJw5k8MOO6xo/xAkRYePPoJbb4UFC4L7TZrAv/4FZ50VaixJklS8xNR12ooDr9MmFQMrVgSzaOPGBfcrVYKHHoIbb4Td7EArSSp4XqdNsS6v3cDfLiQpr7Ztg6eeggcfhN9/D3aBvOEGePhhqFo17HSSJKmYsrRJUl68/Tb06BGcqwZw6qnBUsgTTww3lyRJKvZibiMSSSpSixfD5ZdDy5ZBYTvoIBg1Cv7zHwubJEkqEpY2SdqVtDTo3RsaNYLXXgvOVevZMyhuHTpAvH99SpKkouHySEn6s8zMYOv+vn2Da68BnHceDBkCjRuHGk2SJJVMljZJguD6alOmBLtC/vBD8FijRjBoEFx0UbDpiCRJUggsbZI0bx7ceSd88EFwv3r1YIfILl3cwl+SJIXO30YklVw//wx33w3jxwf3y5WDO+4IZtu8jqIkSYoSljZJJU9aGjzySHDNtYyMYOlju3bB9dbq1Ak7nSRJUi6WNkklxx+bjPTrB2vWBI+dey4MHgwnnBBuNkmSpN2wtEkq/iIRmDo1WPb4/ffBY24yIkmSYoQXGpJUvP3nP3DOOXDZZUFhq1YNnnsOvvoKWrWysEmSpKjnTJuk4umzz+C+++Ctt4L75cpBr17BBbPdZESSJMUQS5uk4uXrr+H+++G114L7pUpB585BgatbN9RokiRJ+8LSJql4WLgQ+vaFl18OzmGLi4O2bYMCd/jhYaeTJEnaZ5Y2SbFt8eLgQthjxkB2dvBYmzZBgWvUKNRokiRJBcHSJik2/fILPPQQDB8O27cHj7VuHRS4Y48NN5skSVIBsrRJii2rVsHAgTB0aHBhbIAWLYKydsop4WaTJEkqBJY2SbFh7Vr4f/8P/vUv2Lw5eKxZs2C27ayzws0mSZJUiCxtkqJbWho8/jg88QRs3Bg81rRpUNbOO8/rrEmSpGLP0iYpOq1eDU89Bc8+GxQ3gOOOg/79vSi2JEkqUSxtkqLLjz/C4MEwcuSOc9YaN4Z+/eCKKyA+Ptx8kiRJRczSJik6fPEFPPooTJy4Y+v+U0+FPn3gkkssa5IkqcSytEkKTyQC778flLUZM3Y8fuGFQVk76yyXQUqSpBLP0iap6GVlwZQpwdb98+YFj8XHw9VXwz//6XXWJEmS/sTSJqnoZGTA2LHB1v3ffx88Vq4cdOkCd9wBhxwSbj5JkqQoZGmTVPjS0yE1Ndi2f8WK4LFKleCWW+DWW6F69VDjSZIkRTNLm6TCs3o1DBkSbNu/YUPwWK1a0KsXdO0KiYmhxpMkSYoFljZJBe+//4Vnngm27d+6NXisQYPgfLXrroOEhHDzSZIkxRBLm6SCkZEBr7wCQ4fCBx/sePyUU4KdIC+91G37JUmS9oGlTdL++emn4Hy1ESPgt9+Cx0qVCq6tdtttcM45btsvSZK0HyxtkvJv+3Z44w147jl4++0dj9euDTfcEOwGWadOePkkSZKKEUubpLxbvhxeeAGefz4Y/6FFC+jWDS6+GEr714okSVJB8rcrSXuWnQ3vvhvMqk2dGlwYG6BqVejcOdgF8rDDws0oSZJUjFnaJO3amjUwahQMGwaLFu14/Kyzglm1K690F0hJkqQiYGmTtEMkAnPmBLNqEyfCtm3B40lJ0L59UNaOOircjJIkSSWMpU0SpKfDiy8G2/V/882Ox084AW66Ca65BipUCC+fJElSCWZpk0qqSAS++CIoauPHw6ZNwePlywcl7aab4KSTws0oSZIkS5tU4ixbFpS0sWNzz6o1ahQUtXbtoFKl0OJJkiQpN0ubVBJs2ACTJwdFbfbsYJYNoGxZuOKKoKyddZYXwZYkSYpCljapuNq2Dd56Kyhq06ZBRsaO584+O5hRu/JKZ9UkSZKinKVNKk4yM4Nrqk2cCK++CuvX73iuceOgqF17LdSrF15GSZIk5YulTYp1eypqNWsGJa1tWzj2WJc/SpIkxSBLmxSLtm2D997bdVGrXj1Y9timTXCeWqlS4eWUJEnSfrO0SbFiw4bgHLUpU4Kf6ek7njvooKCoXXWVRU2SJKmYsbRJ0WzxYpg6Nbh98AFs377jOYuaJElSiWBpk6LJ9u3wySfBTNrUqfD117mfP+ooaN06uJ1yCsTHh5NTkiRJRcbSJoVt6VJ4++3g9s47kJa247lSpYJZtD+K2mGHhZdTkiRJobC0SUVty5ZgqeP06UFR++673M9XrgwXXACXXAIXXQQHHhhOTkmSJEUFS5tU2Fatgrlzd9zmzct9oev4eGjaFFq2hBYt4KSTPD9NkiRJOSxtUkHKzIQvv8xd0pYs2fm4OnWCgtaiBZx/fjC7JkmSJO2CpU3aH6tX5y5o8+cHyx//LC4Ojj4aTjstuJ1+OhxxhBe6liRJUp5Y2qS8ysyEr77KXdIWL975uMqV4dRTd5S0U06BpKSizytJkqRiwdIm7c6vv+58LtquZtGOOmpHQTvtNDjySLfilyRJUoGxtEkQXB/tr7NoP/2083GVKu08i5acXORxJUmSVHJY2lQy/fbbzrNomzfnPiYuDho3zj2L1qCBs2iSJEkqUpY25fbYY3DIIXDZZcWnnGzfDl9/nbuk/fjjzsclJ+eeRWva1Fk0SZIkhc7Sph1++QXuvhu2bYOPPoIzzgg70b757Tf4+OPcs2ibNu183F9n0Ro2LD5FVZIkScWGpU07PPJIUNiaNQu2pY8F27fDN9/knkVbtGjn45KTg5mzP8+iVapU5HElSZKk/LK0KbB8ObzwQjDu1y96ryG2Zk3uWbRPP931LFqjRrln0Ro1chZNkiRJMcnSpsC//hVch+yss+Ccc8JOE8jK2nkW7Ycfdj4uKWnnWbTKlYs+ryRJklQILG0KZqqGDQvGvXqFl2Pt2p1n0X7/fefjGjbceRatVKmizytJkiQVAUubYMwYWL8eDj0ULrmkaD4zKwv++9/cs2jff7/zcYmJO8+iHXhg0WSUJEmSooClraTLzoYnnwzGPXoU3ozVmjXBzNmfZ9E2btz5uAYNcs+iNW7sLJokSZJKNEtbSffBB8EMV1ISdOpUMO+ZkQFffhksdfzkk+C2q+uiVay48yxalSoFk0GSJEkqJixtJd24ccHPq64KliLmVyQCP/20o5x98gl88UVw6YC/atAg98WrjzrKWTRJkiRpLyxtJVlGBkyaFIyvuy5vr9mwIVja+OeStmbNzsdVqRLMnDVtGhS1k092R0dJkiRpH0TVhateeeUVmjdvTpUqVYiLi2PBggU7HZOamso555xDUlIScXFxbNiwYadj1q1bx3XXXUdSUhKVKlWiS5cu/P6nXQhnzZrFpZdeSs2aNalQoQLHHXcc4/6YcdqDd999l9NPP53ExERq1KhB79692b59+/585XC99VZQwmrVCi6o/VeZmfD55/Dcc9CxY7BLY+XK0KIF3H8/vPFGUNjKloVTToFbbw1m7hYtgt9+C56//35o3tzCJkmSJO2jqJpp27RpE2eeeSZt2rThhhtu2OUxmzdvpmXLlrRs2ZKUlJRdHnPdddexcuVKZs6cSWZmJp06daJr166MHz8egDlz5tCkSRN69+7NQQcdxOuvv0779u1JTk7m4osv3uV7fvnll1x00UXcc889jBkzhuXLl9OtWzeysrIYPHhwwfwBFLWXXw5+XnNNcOHppUt3zJ59/HFQ2LZs2fl1hx66YwataVM47jhISCjS6JIkSVJJEReJRCJhh/irJUuWcMghh/DFF19w3HHH7fKYWbNmce6557J+/XoqVaqU8/h3331H48aNmTdvHieddBIA06dP56KLLuKXX36hVq1au3y/Vq1acdBBBzFixIhdPn/33Xczc+ZM5s2bl/PYtGnTaNOmDb/++iuJeTwfLD09neTkZNLS0khKSsrTawpFJALlywdLJA88MJgtW7Vq5+OSk3csc2zaNJhRq1at6PNKkiT9xcqVK0lNTaVr167UrFkz7DhSvuW1G0TVTFtBmDt3LpUqVcopbADnn38+8fHxfPLJJ1x++eW7fF1aWhqNGjXa7ftmZGRQrly5XI+VL1+erVu38tlnn3HOOefs9nUZGRk599PT0/PxbQrR6tVBYQNYty74Wbo0NGmSu6QdeWQwCydJkiQpFMWutK1atYrq1avneqx06dIceOCBrNrVTBLw73//m3nz5jFs2LDdvm+LFi148sknmTBhAm3atGHVqlU8+OCDQPBfeXZnwIAB9OvXbx++SSGrWhW6dQsuqn3yyUFBO+EEOOCAsJNJkiRJ+pPQplDGjRtHxYoVc24ffvhhKDnef/99OnXqxPPPP89RRx212+OaN2/OoEGD6NatGwkJCRx55JFcdNFFAMTvYSYqJSWFtLS0nNuyZcsK/Dvsk9Klgw1GXnoJ7rgDzjzTwiZJkiRFodBKW+vWrVmwYEHO7c/LGfdHjRo1+PXXX3M9tn37dtatW0eNGjVyPT579mwuueQSnnjiCdq3b7/X9+7VqxcbNmxg6dKlrFmzhksvvRSAQw89dLevSUhIICkpKddNkiRJkvIqtOWRiYn/v707j4uq3P8A/hl2GEFBwURwT1xSxKVCUXMJctcsd+2qZZbdm95cIjDMrorVqzLNXFLM3SxKy33BEuWaIggi4oIKbi0qoKIs8v394Y/zcmCGOTOiHLqf9+s1f3Ce5zznOd95znnmO+ecwVX1j3dYIjAwEFlZWYiPj0ebNm0AAHv37kVRURGeeeYZpd6+ffvQu3dvzJ07F+PGjVPdvk6nU37MZN26dfD19UXr1q3LdyeIiIiIiIj+n6aeabt+/ToyMjJw+fJlAEBaWhqA+1fPiq+SXb16FVevXsWZM2cAAMnJyXB1dUWdOnXg4eGBpk2b4oUXXsBrr72GRYsWoaCgAG+99RaGDBmiJFsxMTHo3bs33n77bQwcOFB51s3BwQEeHh4AgB9++AGhoaE4efKk0r+PP/4YL7zwAmxsbBAdHY3IyEh8++23sLW1fTwBIiIiIiKi/zma+lnAzZs3IyAgAL169QIADBkyBAEBAVi0aJFSZ9GiRQgICFD+j1unTp0QEBCAzZs3K3XWrFmDJk2aoFu3bujZsyeCgoKwZMkSpfybb75Bbm4u5syZg1q1aimvF198UamTnZ2tJI3Ftm3bho4dO6Jt27bYsmULNm3ahP79+z+KUBAREREREQHQ6P9p+zvTzP9pIyIiIqrk+H/aqLJTmxto6kobERERERERGWLSRkREREREpGFM2oiIiIiIiDSMSRsREREREZGGMWkjIiIiIiLSMCZtREREREREGsakjYiIiIiISMOYtBEREREREWkYkzYiIiIiIiINY9JGRERERESkYUzaiIiIiIiINIxJGxERERERkYYxaSMiIiIiItIwJm1EREREREQaxqSNiIiIiIhIw5i0ERERERERaRiTNiIiIiIiIg1j0kZERERERKRhTNqIiIiIiIg0jEkbERERERGRhjFpIyIiIiIi0jAmbURERERERBrGpI2IiIiIiEjDmLQRERERERFpGJM2IiIiIiIiDWPSRkREREREpGFM2oiIiIiIiDTMrqI78L9GRAAAOTk5FdwTIiIiosrt5s2buHv3Lm7evAm9Xl/R3SGyWHFOUJwjmKITczWoXF28eBG+vr4V3Q0iIiIiItKIzMxM+Pj4mCxn0vaYFRUV4fLly3B1dYVOp6vQvuTk5MDX1xeZmZlwc3Or0L5UJoybdRg36zF21mHcrMO4WY+xsw7jZh3GzTpai5uI4ObNm/D29oaNjekn13h75GNmY2NTZhZdEdzc3DQxaCsbxs06jJv1GDvrMG7WYdysx9hZh3GzDuNmHS3FrWrVqmbr8IdIiIiIiIiINIxJGxERERERkYYxafsf5ujoiIiICDg6OlZ0VyoVxs06jJv1GDvrMG7WYdysx9hZh3GzDuNmncoaN/4QCRERERERkYbxShsREREREZGGMWkjIiIiIiLSMCZtREREREREGsakjYiIiIiISMOYtFUiM2bMQJMmTaDX6+Hu7o7u3bvj0KFDBnVOnTqFfv36oUaNGnBzc0NQUBBiYmKMtnft2jX4+PhAp9MhKytLWX7lyhUMGzYMjRs3ho2NDSZOnKi6jytWrEDLli3h5OQELy8vTJgwwZpdLVdq4ta3b1/UqVMHTk5OqFWrFkaOHInLly8b1ElKSkLHjh3h5OQEX19ffPTRRwblzz33HHQ6XalXr169TPYtNjYWHTp0QPXq1eHs7IwmTZrgs88+K7+dfwgFBQWYNm0aWrRoAb1eD29vb4waNcogLvv27TO6zzqdDocPHwZwP/7GyvV6vdJOdHQ02rZti2rVqkGv16NVq1ZYtWqV2T7u27cPrVu3hqOjIxo1aoQVK1aUexwspSZu58+fx9ixY1G/fn04OzujYcOGiIiIQH5+vlInLS0NXbp0Qc2aNeHk5IQGDRogPDwcBQUFBtv7/PPP4efnB2dnZ/j6+mLSpEm4e/euyf6peT8qSnR0NIKDg1G9enXodDokJiYalJ8/f97keNu4caNSLyMjA7169YKLiwu8vLwwZcoUFBYWGt3mgQMHYGdnh1atWqnu55kzZ+Dq6opq1apZsZflz1zcAGDJkiV47rnn4ObmVuqcD6g7lgHz50Fj/vWvf6FNmzZwdHS0KM4VJTU1FX379kXVqlWh1+vRrl07ZGRkKOWvv/46GjZsCGdnZ3h6eqJfv344efKkQRvG4rh+/foyt2vJ/K01asbg3bt3MWHCBFSvXh1VqlTBwIED8fvvvxvUsWasGJt7x48fXw579fhERkZCp9MZfN4yN86OHTuGoUOHwtfXF87OzmjatCnmzZtndluVeZwBwM2bNzFx4kTUrVsXzs7OaN++vcE5Cij7GFY7j5SkZow/akzaKpHGjRtjwYIFSE5ORmxsLOrVq4fg4GD8+eefSp3evXujsLAQe/fuRXx8PPz9/dG7d29cvXq1VHtjx45Fy5YtSy3Py8uDp6cnwsPD4e/vr7p/n376KcLCwvDuu+8iJSUFu3fvRkhIiHU7W47UxK1Lly749ttvkZaWhu+//x5nz57FSy+9pJTn5OQgODgYdevWRXx8PD7++GPMmDEDS5YsUepER0fjypUryuv48eOwtbXFyy+/bLJver0eb731Fn799VekpqYiPDwc4eHhBu1WlNzcXBw9ehTTp0/H0aNHER0djbS0NPTt21ep0759e4N9vnLlCl599VXUr18fbdu2BQBMnjy5VJ1mzZoZxMXDwwNhYWGIi4tDUlISRo8ejdGjR2PHjh0m+3fu3Dn06tULXbp0QWJiIiZOnIhXX321zHUeBzVxO3nyJIqKirB48WKkpKTgs88+w6JFi/Dee+8pdezt7TFq1Cjs3LkTaWlp+Pzzz7F06VJEREQoddauXYt3330XERERSE1NxbJly7BhwwaDdkpS835UlNu3byMoKAhz5841Wu7r61uq7x988AGqVKmCHj16AADu3buHXr16IT8/HwcPHsQ333yDFStW4P333y/VXlZWFkaNGoVu3bqp7mNBQQGGDh2Kjh07WreTj4C5uAH3x+ULL7xgcmyoOZbVnAdNGTNmDAYPHmzdDj5GZ8+eRVBQEJo0aYJ9+/YhKSkJ06dPh5OTk1KnTZs2iIqKQmpqKnbs2AERQXBwMO7du2fQVlRUlEE8+/fvX+a2LZm/tUbNGJw0aRJ++uknbNy4Eb/88gsuX76MF198sVQ9a8bKa6+9ZhBrNV8maMXhw4exePHiUp/HzI2z+Ph4eHl5YfXq1UhJSUFYWBhCQ0OxYMGCMrdXmccZALz66qvYtWsXVq1aheTkZAQHB6N79+64dOkSAPPHsJp5xBg1Y/yRE6q0srOzBYDs3r1bRET+/PNPASC//vqrUicnJ0cAyK5duwzWXbhwoXTu3Fn27NkjAOTGjRtGt9G5c2d5++23zfbl+vXr4uzsrPRFy0rGzZhNmzaJTqeT/Px8EbkfL3d3d8nLy1PqTJs2Tfz8/Ey28dlnn4mrq6vcunXLov4NGDBARowYYdE6j8tvv/0mAOTChQtGy/Pz88XT01Nmzpxpso3ExMRS49SYgIAACQ8PN1k+depUad68ucGywYMHS0hISJntVgRzcRMR+eijj6R+/fpltjNp0iQJCgpS/p4wYYJ07drVoM6///1v6dChg+q+qX0/Hqdz584JAElISDBbt1WrVjJmzBjl761bt4qNjY1cvXpVWfbVV1+Jm5ubwfErcn+8hIeHS0REhPj7+6vq29SpU2XEiBESFRUlVatWVbXO46ImbjExMWWe84sZO5atOQ8+yJI4V5TBgwdbfP49duyYAJAzZ84oywDIDz/8oLoNS+ZvLTM1BrOyssTe3l42btyoLEtNTRUAEhcXV6odS8aK2s8pWnTz5k158sknZdeuXWb3w9g4K+nNN9+ULl26mCyv7OMsNzdXbG1t5eeffzZY3rp1awkLCxMR647hkvNIWSyZn8obr7RVUvn5+ViyZAmqVq2qXA2rXr06/Pz8sHLlSty+fRuFhYVYvHgxvLy80KZNG2XdEydOYObMmVi5ciVsbMpnCOzatQtFRUW4dOkSmjZtCh8fHwwaNAiZmZnl0n55MRa3kq5fv441a9agffv2sLe3BwDExcWhU6dOcHBwUOqFhIQgLS0NN27cMNrOsmXLMGTIEItuO0tISMDBgwfRuXNnC/bq8cnOzoZOpzN5W9jmzZtx7do1jB492mQbX3/9NRo3bmzySoWIYM+ePUhLS0OnTp1MthMXF4fu3bsbLAsJCUFcXJz5HXnMzMWtuI6Hh4fJ8jNnzmD79u0GY6N9+/aIj4/Hb7/9BgBIT0/H1q1b0bNnT9V9M/d+aFl8fDwSExMxduxYZVlcXBxatGiBmjVrKstCQkKQk5ODlJQUZVlUVBTS09MNrlyas3fvXmzcuBFffvll+eyAhhk7lq05D1YmRUVF2LJlCxo3boyQkBB4eXnhmWeewY8//mhyndu3byMqKgr169eHr6+vQdmECRNQo0YNPP3001i+fDmkjH+Lq3b+rqzi4+NRUFBgcM5u0qQJ6tSpUy7n7DVr1qBGjRp46qmnEBoaitzc3Idu83GYMGECevXqVWouK6mscfYgc/NIZR9nhYWFuHfvnsGVbwBwdnZGbGysVcewsXlEsx57mkgP5aeffhK9Xi86nU68vb3lt99+MyjPzMyUNm3aiE6nE1tbW6lVq5YcPXpUKb979660bNlSVq1aJSLmv3VV+w3WnDlzxN7eXvz8/GT79u0SFxcn3bp1Ez8/v1LfblcEc3ETuf8NuouLiwCQZ599Vv766y+l7Pnnn5dx48YZ1E9JSREAcuLEiVJtHTp0SADIoUOHVPWvdu3a4uDgIDY2NmVepapId+7ckdatW8uwYcNM1unRo4f06NGjzDbc3d1l7ty5pcqysrJEr9eLnZ2dODo6yrJly8rsz5NPPimzZ882WLZlyxYBILm5uWb25vFRE7fTp0+Lm5ubLFmypFRZYGCgODo6CgAZN26c3Lt3z6B83rx5Ym9vL3Z2dgJAxo8fb1HfTL0fFUntN5lvvPGGNG3a1GDZa6+9JsHBwQbLbt++LQBk69atIiJy6tQp8fLykrS0NBFR963+X3/9Jb6+vvLLL7+IiPztr7QZO5YtPQ+WpPUrbVeuXBEA4uLiIp9++qkkJCTInDlzRKfTyb59+wzqfvnll6LX6wWA+Pn5lbr6MXPmTImNjZWjR49KZGSkODo6yrx588rcvrn5uzIwNQbXrFkjDg4Opeq3a9dOpk6dWmq5JWNl8eLFsn37dklKSpLVq1dL7dq1ZcCAAdZ0/7Fat26dPPXUU3Lnzh0RMf55y9w4e9CBAwfEzs5OduzYUeZ2K/s4CwwMlM6dO8ulS5eksLBQVq1aJTY2NtK4cWOLjuFixuaRslTklTYmbRq1evVq0ev1yqv4UvatW7fk9OnTEhcXJ2PGjJF69erJ77//LiIiRUVF0rdvX+nRo4fExsZKfHy8vPHGG1K7dm25fPmyiNy/vWrw4MHKdsoraZs1a5YAMDhZ/PHHH2JjYyPbt2+3MgqWsyZuxf78809JS0uTnTt3SocOHaRnz55SVFQkIpZ/WBk3bpy0aNFCdb/T09MlKSlJlixZIh4eHrJ27VpLd/2hmYqdyP1bpfr06SMBAQGSnZ1tdP3MzEyxsbGR7777zuQ21q5dK3Z2dga3rhW7d++enD59WhISEuSTTz6RqlWrSkxMjMm2tJK0PWzcLl68KA0bNpSxY8caLc/IyJCUlBRZu3at1K5d2yDBiomJkZo1a8rSpUslKSlJoqOjxdfXV3XiX9b78aiVFTc1k2Jubq5UrVpVPvnkE4Pl5pK2wsJCadu2rXz11VdKuZoPiAMGDJBp06Ypf1dU0vawcVOTtJk6lv9uSVvJWO7bt08AyNChQw3q9enTR4YMGWKwLCsrS06dOiW//PKL9OnTR1q3bq18+DZm+vTp4uPjY7JczfytFdaMwUeZtJVU/NhHWQlORcvIyBAvLy85duyYsszY5y214yw5OVlq1KghH374YZnbrUzjzJQzZ85Ip06dBIDY2tpKu3btZPjw4dKkSRO5dOmS6mNYxPQ8UhYmbVRKTk6OnD59WnmZ+hDaqFEj5YPr7t27xcbGptSHw0aNGsmcOXNERMTf319sbGzE1tZWbG1txcbGRhn477//fqn21SZty5cvFwCSmZlpsNzLy8vo1YNHxZq4GZOZmSkA5ODBgyIiMnLkSOnXr59Bnb179woAuX79usHyW7duiZubm3z++edW7cOHH34ojRs3tmrdh2Eqdvn5+dK/f39p2bKlwdXHkmbOnCmenp7Kc4DGdO3aVfr376+qP2PHji314ftBHTt2LDU2ly9fLm5ubqraLy8PE7dLly7Jk08+KSNHjix1Bc2YVatWibOzsxQWFoqISFBQkEyePNloHTXtWfJ+lLeyjlU1k+LKlSvF3t5e/vjjD4Pl06dPL/VhLz09XQDI0aNH5caNG8o5r/il0+mUZXv27DG6vapVqxqs8+C509xV4fL0sHFTk7SZOpYtOQ8ao7WkrWQss7KyxM7OrtQH36lTp0r79u1NtpOXlycuLi5lftn2888/CwC5e/eu0XI187dWWDMGTT0/X6dOHfn0009LbeNhxsqtW7cEwGP9wthSP/zwQ6nzEADl6lfxOf5BpsZZSkqKeHl5yXvvvWd2u5VpnJlz69YtJdEcNGiQ9OzZU/Ly8iw6hk3NI2WpyKTNrnxvtqTy4urqCldXV7P1ioqKkJeXBwDKPdwln1OzsbFBUVERAOD777/HnTt3lLLDhw9jzJgx2L9/Pxo2bGh1fzt06ADg/s+U+/j4ALj/bNhff/2FunXrWt2upayJm6lyAEqdwMBAhIWFoaCgQHnObdeuXfDz84O7u7vBuhs3bkReXh5GjBhh1T6Y69ujYix2BQUFGDRoEE6fPo2YmBhUr17d6LoigqioKIwaNUqJT0nnzp1DTEwMNm/erKo/5uIQGBiIrVu3GizbtWsXAgMDVbVfXqyN26VLl9ClSxflF8LUPF9aVFSEgoICFBUVwdbWFrm5uaXWs7W1BYAyn58BLH8/ypvaY9WUZcuWoW/fvvD09DRYHhgYiFmzZuGPP/6Al5cXgPvjws3NDc2aNYO9vT2Sk5MN1lm4cCH27t2L7777DvXr1ze6vbi4OINfB9y0aRPmzp2LgwcPonbt2lbvh6UeNm7mlHUsW3IerAyMxbJdu3ZIS0szWHbq1Kky5zG5/wV4meerxMREuLu7w9HR0Wi5mvlbK6wZg23atIG9vT327NmDgQMHArj/eSEjI6Pcz9nFP8Veq1atcm23PHXr1q3UeWj06NFo0qQJpk2bppzHH2RsnKWkpKBr16545ZVXMGvWLLPbrUzjzBy9Xg+9Xo8bN25gx44d+Oijj+Dg4GDRMWxqHtGsx54mklVu3boloaGhEhcXJ+fPn5cjR47I6NGjxdHRUY4fPy4i92/vq169urz44ouSmJgoaWlpMnnyZLG3t5fExESj7Zr61jUhIUESEhKkTZs2MmzYMElISJCUlBSlPDo6utQvhvXr10+aN28uBw4ckOTkZOndu7c0a9aszCsvj5qauP33v/+V+fPnS0JCgpw/f1727Nkj7du3l4YNGyrfimZlZUnNmjVl5MiRcvz4cVm/fr24uLjI4sWLS20zKCjI4BbUB7377rsycuRI5e8FCxbI5s2b5dSpU3Lq1Cn5+uuvxdXVVfkVpIqUn58vffv2FR8fH0lMTJQrV64or5LPKe7evVsASGpqqsn2wsPDxdvb2+g3iLNnz5adO3fK2bNn5cSJE/LJJ5+InZ2dLF26VKlTMnbp6eni4uIiU6ZMkdTUVPnyyy/F1ta2wr9dVRO3ixcvSqNGjaRbt25y8eJFgzrFVq9eLRs2bJATJ07I2bNnZcOGDeLt7S3Dhw9X6kRERIirq6usW7dO0tPTZefOndKwYUMZNGiQUmf+/PmlfmFSpOz3o6Jcu3ZNEhISlNtc169fLwkJCQZxEbn/DKBOp5Nt27aVaqOwsFCeeuopCQ4OlsTERNm+fbt4enpKaGioye0a+1bfVNyKaemZNjVxu3LliiQkJMjSpUuVX49LSEiQa9euGbRV1rGs5jxobG4ovu359ddfl8aNGyvzixaedy4pOjpa7O3tZcmSJXL69GmZP3++2Nrayv79+0VE5OzZszJ79mw5cuSIXLhwQQ4cOCB9+vQRDw8P5Zb7zZs3y9KlSyU5OVlOnz4tCxcuFBcXF4O7WQ4dOiR+fn5y8eJFEbFu/tYSNWNw/PjxUqdOHdm7d68cOXJEAgMDJTAw0KAdc2Pl4sWL4ufnpzwvfubMGZk5c6YcOXJEzp07J5s2bZIGDRpIp06dHt/Ol5MH72xSM86Sk5PF09NTRowYYTCHPHjV6O82zkREtm/fLtu2bVPmPH9/f3nmmWeUz5rmjuFiZc0jIiJ+fn4SHR2t/K12fnqUmLRVEnfu3JEBAwaIt7e3ODg4SK1ataRv376lflDj8OHDEhwcLB4eHuLq6irPPvus8vC9MaaSNgClXnXr1lXKo6KipGTOn52dLWPGjJFq1aqJh4eHDBgwQDIyMh563x+GmrglJSVJly5dxMPDQxwdHaVevXoyfvx45SRX7NixYxIUFCSOjo5Su3ZtiYyMLLW9kydPCgDZuXOn0f688sor0rlzZ+XvL774Qpo3by4uLi7i5uYmAQEBsnDhQlW3tj1qxbcAGHuVfNZs6NChZd4+dO/ePfHx8TF5+0ZYWJg0atRInJycxN3dXQIDA2X9+vUGdUrGTuT++G3VqpU4ODhIgwYNJCoqyppdLVdq4lZ8/Bh7FVu/fr20bt1aqlSpInq9Xpo1ayazZ882eJ6hoKBAZsyYIQ0bNhQnJyfx9fWVN9980+B4joiIMDh2Rcy/HxXFVFwiIiIM6oWGhoqvr6/J4+T8+fPSo0cPcXZ2lho1asg777wjBQUFJrdrLGkzFreSfdVK0qYmbhEREUbrlDxmzB3L5s6DxuaGzp07G932uXPnHnbXH4lly5Yp5yN/f3/58ccflbJLly5Jjx49xMvLS+zt7cXHx0eGDRsmJ0+eVOps27ZNWrVqpRy7/v7+smjRIoPxWjz3PhgDS+dvLVEzBu/cuSNvvvmmuLu7i4uLiwwYMKDUB15zY6X4/Fp8Ls3IyJBOnTop83ejRo1kypQpJp8h1rIHkzY148zUMf3geevvNs5ERDZs2CANGjQQBwcHeeKJJ2TChAmSlZVlUKesY7iYuXmk5PlR7fz0KOn+v2NERERERESkQfw/bURERERERBrGpI2IiIiIiEjDmLQRERERERFpGJM2IiIiIiIiDWPSRkREREREpGFM2oiIiIiIiDSMSRsREREREZGGMWkjIiIiIiLSMCZtREREj9iePXvQtGlT3Lt3DwAwY8YMtGrV6qHbzc/PR7169XDkyJGHbouIiLSLSRsREZEZ//jHP6DT6RAZGWmw/Mcff4ROpzO7/tSpUxEeHg5bW1tV29u3bx90Oh2aN2+uJHrFqlWrhhUrVgAAHBwcMHnyZEybNk3djhARUaXEpI2IiEgFJycnzJ07Fzd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tiTIAAAC4NIIMjqVhw7goW7LELI1PlAEAAMBFEWRwPA0aSMuWmShbutScTPrWLaunAgAAAFIdQQbH9MIL0vLlkre3ibPWrYkyAAAAuByCDI6rXr24KFu+XGrViigDAACASyHI4Njq1pVWrJB8fMzPli2lqCirpwIAAABSBUEGxxccHBdlK1dKLVoQZQAAAHAJBBmcQ5060qpVkq+v9M03UvPm0s2bVk8FAAAAPBSCDM6jVq24KPv2W6IMAAAATo8gg3OpWdPsIfP1lVavlpo1I8oAAADgtAgyOJ8aNcweskcekdaskZo0kW7csHoqAAAAINkIMjinoKC4KPvuO6IMAAAATokgg/N6/nlz2GKmTFJoqNS4sXT9utVTAQAAAElGkMG5Va8eF2Xr1hFlAAAAcCoEGZzfc8+Z75JlziytXy81bChdu2b1VAAAAMADEWRwDdWqSWvXSn5+0saNRBkAAACcAkEG11GlSlyUbdokNWhAlAEAAMChEWRwLZUrm1UX/f2lzZulF16Qrl61eioAAAAgQQQZXM+zz8ZF2ZYtRBkAAAAcFkEG1/TMM2Yp/IAAaetWqX596coVq6cCAAAA4iHI4LqefjouyrZtk+rVkyIjrZ4KAAAAiEWQwbU99ZQ5P1mWLNL27UQZAAAAHApBBtf35JMmyrJmlXbskOrWlSIirJ4KAAAAIMjgJipViouynTuJMgAAADgEggzuo2JFaf16KVs2adcuKThYunzZ6qkAAADgxggyuJcKFeKibPduogwAAACWIsjgfp54QtqwQQoMlPbskerUkS5dsnoqAAAAuCGCDO6pfHkTZdmzS3v3EmUAAACwhMMF2ahRo2Sz2eJdSpYsKUk6ceLEPb+LuSxevDj2Ofbs2aNatWopS5Ysypo1q+rWrasff/zxvq979uxZdezYUblz51amTJlUsWJFLVmyJE3fKyxWrlxclO3bJ9WuLV28aPVUAAAAcCMOF2SSVKZMGYWHh8detm3bJknKnz9/vPvDw8M1evRoZc6cWfXr15ckXb16VfXq1VOBAgW0a9cubdu2TX5+fqpbt65u376d6Gt26tRJYWFhWrFihQ4ePKjmzZurdevW+uGHH9LlPcMijz8ubdwo5cgh7d8v1aol/f231VMBAADATThkkHl5eSl37tyxl+zZs0uSPD09492fO3duLVu2TK1bt1bmzJklSUeOHNHFixf11ltvqUSJEipTpoxGjhypc+fO6Y8//kj0Nbdv365XXnlFTz/9tIoUKaJhw4YpS5Ys2rdvX6KPiYqKUmRkZLwLnFDZsibKcuaUDhwwe8qIMgAAAKQDhwyy3377TXnz5lWRIkXUvn17nTx5MsHt9u3bpwMHDqhbt26x95UoUUKBgYGaOXOmbt26pRs3bmjmzJkqVaqUChUqlOhrVqlSRQsXLtTFixcVHR2tBQsW6ObNmwoKCkr0MWPGjFFAQEDsJX/+/Cl9y7BamTImynLlMlFWq5Z04YLVUwEAAMDFOVyQPfPMM5ozZ47WrFmjadOm6fjx43ruued05cqVe7aNCa0qVarE3ufn56dNmzZp7ty58vX1VebMmbVmzRqtXr1aXl5eib7uokWLdPv2bQUGBsrb21u9evXSsmXLVKxYsUQfM2TIEEVERMReTp069XBvHtYqXTouyn78UapZU/rrL6unAgAAgAtzuCCrX7++WrVqpXLlyqlu3br69ttvdfnyZS1atCjedjdu3NC8efPi7R2Lub9bt26qWrWqdu7cqe+//15ly5ZVgwYNdOPGjURfd/jw4bp8+bLWrVunvXv3auDAgWrdurUOHjyY6GO8vb3l7+8f7wInV6qUtGmTlDu3dPCg2VNGlAEAACCNJL7LyEFkyZJFxYsX19GjR+Pd/9VXX+n69evq1KlTvPvnzZunEydOaMeOHfLw8Ii9L2vWrFq+fLnatGlzz2scO3ZMU6dO1aFDh1SmTBlJUvny5bV161Z99NFH+uSTT9Lo3cEhlSxpoqxGDRNlNWuak0nnzGn1ZAAAAHAxDreH7N+uXr2qY8eOKU+ePPHunzlzpho3bqwcOXLEu//69evy8PCQzWaLvS/mdnR0dIKvcf369djt/snT0zPRx8DFlShhoixvXunQIRNn585ZPRUAAABcjMMF2aBBg7R582adOHFC27dvV7NmzeTp6am2bdvGbnP06FFt2bJF3bt3v+fxderU0aVLl9S3b18dPnxYP//8s7p06SIvLy/VqFFDknT69GmVLFlSu3fvliSVLFlSxYoVU69evbR7924dO3ZMH3zwgUJDQ9W0adN0ed9wQMWLmyh79FHpl1/MnjKiDAAAAKnI4YLszz//VNu2bVWiRAm1bt1agYGB2rlzZ7w9YbNmzVK+fPkUHBx8z+NLliyplStX6qefflLlypX13HPP6cyZM1qzZk3sXrbbt28rLCwsds9YhgwZ9O233ypHjhxq1KiRypUrp88//1whISF64YUX0ueNwzE99lj8KKtRQzp71uqpAAAA4CJsdrvdbvUQriIyMlIBAQGKiIhggQ9Xc/SoibE//zTfMduwQfrXYbQAACD1hIeHa8aMGerZs+c9X10BnEFS28Dh9pABDqlYMbOnLH9+6cgRE2fh4VZPBQAAACdHkAFJVbSoibICBaSwMCkoSDpzxuqpAAAA4MQIMiA5ihQxUVawoPTrrybKTp+2eioAAAA4KYIMSK7CheOi7LffTJT9+afVUwEAAMAJEWRAShQqJG3ebH4ePWqi7NQpi4cCAACAsyHIgJQqWNDsKStcWDp2jCgDAABAshFkwMOIibIiRaTffzdRdvKk1VMBAADASRBkwMMqUMBEWdGicVH2xx9WTwUAAAAnQJABqSF//rgoO37cRNmJExYPBQAAAEdHkAGpJV8+s9DHY4+ZGCPKAAAA8AAEGZCaHn1U2rjRRNkff0jPP2/2mAEAAAAJIMiA1Pboo+bwxeLFzQIfzz9vvlsGAAAA/AtBBqSFvHlNlJUoYZbCDwoyS+MDAAAA/0CQAWklTx4TZSVLxkXZ0aNWTwUAAAAHQpABaSl3bvOdslKlpD//NFH2229WTwUAAAAHQZABaS0mykqXlk6fJsoAAAAQiyAD0kOuXCbKypSRzpwxC32EhVk9FQAAACxGkAHpJWdOacMGqWxZKTxcqlGDKAMAAHBzBBmQnmKi7PHHTZQFBUlHjlg9FQAAACxCkAHpLUcOE2Xlyklnz5ooO3zY6qkAAABgAYIMsEL27NL69VL58tK5cybKfvnF6qkAAACQzggywCoxUfbEE9L58+Y7ZT//bPVUAAAASEcEGWClwEATZRUqxEXZoUNWTwUAAIB0QpABVsuWTVq3TqpYUfrrLxNlBw9aPRUAAADSAUEGOIKYKKtUSbpwQapZU/rpJ6unAgAAQBojyABHkTWribInn4yLsh9/tHoqAAAApCGCDHAkWbJIoaHSU09Jf/9touzAAaunAgAAQBohyABHExNlzzwjXbwo1aol/fCD1VMBAAAgDRBkgCMKCJDWrpWefTYuyvbvt3oqAAAApDKCDHBUMVFWubJ06ZKJsn37rJ4KAAAAqYggAxyZv7+0Zo1UpYp0+bJUu7a0d6/VUwEAACCVEGSAo4uJsqpV46Jszx6rpwIAAEAqIMgAZ+DnJ61eLVWrJkVEmCjbvdvqqQAAAPCQCDLAWcRE2XPPSZGRUp060q5dVk8FAACAh0CQAc4kc2bp22+l6tXjomzHDqunAgAAQAoRZICziYmyoCDpyhWpbl1p+3arpwIAAEAKEGSAM8qUSVq1SqpRIy7Kvv/e6qkAAACQTAQZ4KxioqxmTenqValePWnbNqunAgAAQDIQZIAze+QRaeVKc9LomCjbutXqqQAAAJBEBBng7GKirE4d6do1qX59acsWq6cCAABAEhBkgCvw9ZWWL5eCg+OibPNmq6cCAADAAxBkgKvw9ZW+/tos8HH9uvTCC9KmTVZPBQAAgPsgyABXEhNl9erFRdmGDVZPBQAAgEQQZICr8fGRli0zMXbjhtSwobR+vdVTAQAAIAEEGeCKfHykpUulBg3iomzdOqunAgAAwL8QZICr8vaWliwxMXbzptSokRQaavVUAAAA+AeCDHBl3t7SV1+ZGIuJsrVrrZ4KAAAA/0OQAa4uJsqaNJGioszPNWusngoAAAAiyAD3kDGjtGiR1LSpibKmTaXVq62eCgAAwO0RZIC7iImyZs3iouzbb62eCgAAwK0RZIA7yZBBWrhQatFCunXLxNmqVVZPBQAA4LYIMsDdZMggzZ8vtWxpoqx5c2nlSqunAgAAcEsEGeCOMmSQ5s2TWrWSbt82e8xWrLB6KgAAALdDkAHuKibKXnzRRFnLltLy5VZPBQAA4FYIMsCdeXlJc+dKbdrERdmyZVZPBQAA4DYIMsDdeXlJX3whtW0r3bkjtW4tLV1q9VQAAABugSADYKLs88+l9u3jomzJEqunAgAAcHkEGQDDy0sKCZE6dJDu3jXfLVu82OqpAAAAXJqX1QMAcCCentKcOZLNFncYo91u9pgBAAAg1bGHDEB8np7S7NlS585mT1m7duZk0gAAAEh1BBmAe3l6SjNnSl26xEXZ/PlWTwUAAOByCDIACfP0lD77TOraVYqONt8tmzfP6qkAAABcCkEGIHEeHtKnn0rdu5so69jRnLcMAAAAqYIgA3B/Hh7S9OlSjx4myjp3Ngt+AAAA4KERZAAezMND+uQTqWfPuCgLCbF6KgAAAKdHkAFIGg8Pado0qXdvsxR+ly5miXwAAACkmEMH2dixY2Wz2dS/f//Y+86ePauOHTsqd+7cypQpkypWrKglS5bEe1yhQoVks9niXcaOHXvf1+rVq5eKFi0qX19f5ciRQ02aNNGRI0fS4m0BzsvDQ/r4Y6lPHxNlXbuaJfIBAACQIg4bZHv27NH06dNVrly5ePd36tRJYWFhWrFihQ4ePKjmzZurdevW+uGHH+Jt99Zbbyk8PDz28sorr9z39SpVqqTZs2fr8OHDWrt2rex2u4KDg3X37t1Uf2+AU7PZpI8+kvr2NVHWrZtZIh8AAADJ5pBBdvXqVbVv316ffvqpsmbNGu9327dv1yuvvKKnn35aRYoU0bBhw5QlSxbt27cv3nZ+fn7KnTt37CVTpkz3fc2ePXuqevXqKlSokCpWrKh33nlHp06d0okTJ1L77QHOz2aTpkyRXnnFRFn37maJfAAAACSLQwZZ37591aBBA9WuXfue31WpUkULFy7UxYsXFR0drQULFujmzZsKCgqKt93YsWMVGBioChUqaPz48bpz506SX//atWuaPXu2ChcurPz58ye6XVRUlCIjI+NdALdhs0mTJ0v9+pnbPXpIM2ZYOxMAAICT8bJ6gH9bsGCB9u/frz179iT4+0WLFunFF19UYGCgvLy89Mgjj2jZsmUqVqxY7Db9+vVTxYoVlS1bNm3fvl1DhgxReHi4Jk6ceN/X/vjjj/XGG2/o2rVrKlGihEJDQ5UxY8ZEtx8zZoxGjx6dsjcKuAKbTfrww7g469XL7DHr1cvqyQAAAJyCQ+0hO3XqlF599VV9+eWX8vHxSXCb4cOH6/Lly1q3bp327t2rgQMHqnXr1jp48GDsNgMHDlRQUJDKlSun3r1764MPPtCUKVMUFRV139dv3769fvjhB23evFnFixdX69atdfPmzUS3HzJkiCIiImIvp06dStkbB5yZzSZNmiQNGGBu9+5tVmMEAADAA9nsdrvd6iFifP3112rWrJk8PT1j77t7965sNps8PDwUFhamYsWK6dChQypTpkzsNrVr11axYsX0ySefJPi8P//8s8qWLasjR46oRIkSSZrl1q1bypo1qz777DO1bds2SY+JjIxUQECAIiIi5O/vn6THAC7Dbpdef1364ANz+6OPpJdesnYmAIDTCg8P14wZM9SzZ0/lyZPH6nGAZEtqGzjUIYu1atWKt6dLkrp06aKSJUvqzTff1PXr1yVJHh7xd+x5enoqOjo60ec9cOCAPDw8lDNnziTPYrfbZbfbH7hXDcD/2GzS+PHm54QJZhXG6Gjp5ZetngwAAMBhOVSQ+fn5qWzZsvHuy5QpkwIDA1W2bFndvn1bxYoVU69evTRhwgQFBgbq66+/VmhoqFatWiVJ2rFjh3bt2qUaNWrIz89PO3bs0IABA9ShQ4fYFRtPnz6tWrVq6fPPP9fTTz+t33//XQsXLlRwcLBy5MihP//8U2PHjpWvr69eeOGFdP8cAKdls0nvv2/OV/b++3GrMD7gtBMAAADuyqGC7EEyZMigb7/9VoMHD1ajRo109epVFStWTCEhIbHh5O3trQULFmjUqFGKiopS4cKFNWDAAA0cODD2eW7fvq2wsLDYPW4+Pj7aunWrPvzwQ126dEm5cuVS9erVtX379mTtVQMgE2Vjx5ooGzvWrMIYHS29+qrVkwEAADgch/oOmbPjO2TAP9jt0tCh0pgx5vbEiXELfwAA8AB8hwzOLqlt4FCrLAJwITab9O67JsokaeBAE2UAAACIRZABSDs2m/T229Lw4eb2a6/FrcIIAAAAggxAGrPZpNGjpREjzO1Bg8xqjAAAACDIAKSDmCgbNcrcfuMNadw4S0cCAABwBAQZgPQzcqQJM0kaPNiswggAAODGCDIA6WvECOmtt8z1IUOk996zdh4AAAALEWQA0t/w4dI775jrQ4ea1RgBAADcEEEGwBpDh8btHRs2zKzGCAAA4GYIMgDWGTIk7sTRI0bEfb8MAADATRBkAKw1eHDcioujRsWtxAgAAOAGCDIA1nvjjbhzk40ebVZjtNutnQkAACAdEGQAHMOgQdKECeb6W2+ZQxiJMgAA4OIIMgCO47XXpIkTzfV33jGrMRJlAADAhRFkABzLgAHSpEnm+rvvmtUYiTIAAOCiCDIAjqd/f2nyZHN9zBizGiNRBgAAXBBBBsAx9esnTZliro8bZ1ZjJMoAAICLIcgAOK6XX5amTjXX33/frMZIlAEAABdCkAFwbH37Sh99ZK5PmGBWYyTKAACAiyDIADi+l16Spk0z1ydONKsxEmUAAMAFEGQAnEPv3tL06eb6pElmNUaiDAAAODmCDIDz6NlTmjHDXJ882azGSJQBAAAnRpABcC49ekiffSbZbNJ//2tWYyTKAACAkyLIADifbt3iomzqVLMaI1EGAACcEEEGwDl17SrNnGmi7OOPzWqM0dFWTwUAAJAsBBkA59WlizR7tomyadOIMgAA4HQIMgDOrXNnac4cE2WffCL16UOUAQAAp0GQAXB+nTpJn38ueXiYVRh79SLKAACAUyDIALiGDh3iouyzz8wS+UQZAABwcAQZANfRvr30xRcmymbOlLp3J8oAAIBDI8gAuJZ27aQvvzRRNnu2WSL/7l2rpwIAAEgQQQbA9bRpI82bJ3l6mgU/iDIAAOCgCDIArunFF6X5802UhYSYJfKJMgAA4GC8rB4AANJMq1ZmOfw2bcx3y+x2s8fM09PqyQAAACSxhwyAq2vZUlq4UPLykubONUvk37lj9VQAAACSCDIA7qBFC2nRIhNl8+YRZQAAwGEQZADcQ7Nm0uLFJsrmz5c6diTKAACA5QgyAO6jaVPpq6+kDBmkBQvMecuIMgAAYCGCDIB7adJEWrLERNmiRea8ZbdvWz0VAABwUwQZAPfTqJG0dKmUMaM5jLFtW6IMAABYgiAD4J4aNoyLsiVLzNL4RBkAAEhnBBkA99WggbRsmYmypUvNyaRv3bJ6KgAA4EYIMgDu7YUXpOXLJW9vE2etWxNlAAAg3RBkAFCvXlyULV8utWpFlAEAgHRBkAGAJNWtK61YIfn4mJ8tW0pRUVZPBQAAXBxBBgAxgoPjomzlSqlFC6IMAACkKYIMAP6pTh1p1SrJ11f65hupeXPp5k2rpwIAAC6KIAOAf6tVKy7Kvv2WKAMAAGmGIAOAhNSsafaQ+fpKq1dLzZoRZQAAINURZACQmBo1zB6yRx6R1qyRmjSRbtyweioAAOBCCDIAuJ+goLgo++47ogwAAKQqggwAHuT5581hi5kySaGhUuPG0vXrVk8FAABcAEEGAElRvbo5bDFzZmndOqIMAACkCoIMAJKqWrW4KFu/XmrYULp2zeqpAACAEyPIACA5qlaV1q6V/PykjRuJMgAA8FAIMgBIripV4qJs0yapQQOiDAAApAhBBgApUbmyWXXR31/avFl64QXp6lWrpwIAAE6GIAOAlHr22bgo27KFKAMAAMlGkAHAw3jmGbMUfkCAtHWrVL++dOWK1VMBAAAnQZABwMN6+um4KNu2TapXT4qMtHoqAADgBAgyAEgNTz1lzk+WJYu0fTtRBgAAkoQgA4DU8uSTJsqyZpV27JDq1pUiIqyeCgAAODCCDABSU6VKcVG2cydRBgAA7osgA4DUVrGitH69lC2btGuXFBwsXb5s9VQAAMABEWQAkBYqVIiLst27iTIAAJAgggwA0soTT0gbNkiBgdKePVKdOtKlS1ZPBQAAHAhBBgBpqXx5E2XZs0t79xJlAAAgHoIMANJauXJxUbZvn1S7tnTxotVTAQAAB+BQQTZt2jSVK1dO/v7+8vf3V+XKlbV69erY3x87dkzNmjVTjhw55O/vr9atW+vcuXPxnuPdd99VlSpV9MgjjyhLlixJet2rV6/q5ZdfVr58+eTr66vSpUvrk08+Sc23BsDdPf64tHGjlCOHtH+/VKuW9PffVk8FAAAs5lBBli9fPo0dO1b79u3T3r17VbNmTTVp0kQ///yzrl27puDgYNlsNm3YsEHff/+9bt26pUaNGik6Ojr2OW7duqVWrVqpT58+SX7dgQMHas2aNZo7d64OHz6s/v376+WXX9aKFSvS4m0CcFdly5ooy5lTOnDA7CkjygAAcGs2u91ut3qI+8mWLZvGjx+v/Pnzq379+rp06ZL8/f0lSREREcqaNau+++471a5dO97j5syZo/79++tyElY1K1u2rF588UUNHz489r5KlSqpfv36euedd5I8a2RkpAICAhQRERE7IwDc45dfpJo1pXPnzHfM1q0zhzMCAGKFh4drxowZ6tmzp/LkyWP1OECyJbUNHGoP2T/dvXtXCxYs0LVr11S5cmVFRUXJZrPJ29s7dhsfHx95eHho27ZtD/VaVapU0YoVK3T69GnZ7XZt3LhRv/76q4KDg+/7uKioKEVGRsa7AMADlS5t9pTlyiX9+KOJs7/+snoqAABgAYcLsoMHDypz5szy9vZW7969tWzZMpUuXVrPPvusMmXKpDfffFPXr1/XtWvXNGjQIN29e1fh4eEP9ZpTpkxR6dKllS9fPmXMmFH16tXTRx99pOrVq9/3cWPGjFFAQEDsJX/+/A81BwA3UqqUtGmTlDu3dPCg+U4ZUQYAgNtxuCArUaKEDhw4oF27dqlPnz7q3LmzfvnlF+XIkUOLFy/WypUrlTlzZgUEBOjy5cuqWLGiPDwe7m1MmTJFO3fu1IoVK7Rv3z598MEH6tu3r9atW3ffxw0ZMkQRERGxl1OnTj3UHADcTMmSJsry5DFRVrOmdP681VMBAIB05GX1AP+WMWNGFStWTJL5HteePXs0efJkTZ8+XcHBwTp27JguXLggLy8vZcmSRblz51aRIkVS/Ho3btzQf/7zHy1btkwNGjSQJJUrV04HDhzQhAkT7vlu2j95e3vHO4QSAJKtRAkTZTVqSIcOmZ8bNpjDGQEAgMtzuD1k/xYdHa2oqKh492XPnl1ZsmTRhg0bdP78eTVu3DjFz3/79m3dvn37nr1snp6e8VZvBIA0U7y4ibJHH42/4AcAAHB5DrWHbMiQIapfv74KFCigK1euaN68edq0aZPWrl0rSZo9e7ZKlSqlHDlyaMeOHXr11Vc1YMAAlShRIvY5Tp48qYsXL+rkyZO6e/euDhw4IEkqVqyYMmfOLEkqWbKkxowZo2bNmsnf31/PP/+8Xn/9dfn6+qpgwYLavHmzPv/8c02cODHdPwMAbuqxx0yUBQWZKIvZU5Y7t9WTAQCANORQQXb+/Hl16tRJ4eHhCggIULly5bR27VrVqVNHkhQWFqYhQ4bo4sWLKlSokIYOHaoBAwbEe44RI0YoJCQk9naFChUkSRs3blRQUFDs80RERMRus2DBAg0ZMkTt27fXxYsXVbBgQb377rvq3bt3Gr9jAPiHYsXiDl88fDguyljuGQAAl+Xw5yFzJpyHDECqOHbMxNipU+Y7Zhs3EmUA3A7nIYOzc/rzkAGA2ypa1OwpK1BACgszhzGeOWP1VAAAIA0QZADgiIoUMVFWsKD0668myk6ftnoqAACQyggyAHBUhQvHRdlvv5ko+/NPq6cCAACpiCADAEdWqJC0ebP5efSoiTJOQg8AgMsgyADA0RUsaPaUFS5sFvwICpJOnrR6KgAAkAoIMgBwBjFRVqSI9PvvRBkAAC6CIAMAZ1GggImyokWl48dNlP3xh9VTAQCAh0CQAYAzyZ//3ig7ccLioQAAQEoRZADgbPLlMwt9PPaYiTGiDAAAp0WQAYAzevRRaeNGE2V//CE9/7zZYwYAAJwKQQYAzurRR83hi8WLmwU+nn/eLPgBAACcBkEGAM4sb14TZSVKmPOTBQWZpfEBAIBTIMgAwNnlyWOirGTJuCg7etTqqQAAQBIQZADgCnLnNt8pK1VK+vNPE2W//Wb1VAAA4AEIMgBwFTFRVrq0dPo0UQYAgBMgyADAleTKZaKsTBnpzBmz0EdYmNVTAQCARBBkAOBqcuaUNmyQypaVwsOlGjWIMgAAHBRBBgCuKCbKHn/cRFlQkHTkiNVTAQCAfyHIAMBV5chhoqxcOensWRNlhw9bPRUAAPgHggwAXFn27NL69VL58tK5cybKfvnF6qkAAMD/EGQA4OpiouyJJ6Tz5813yn7+2eqpAACACDIAcA+BgSbKKlSIi7JDh6yeCgAAt0eQAYC7yJZNWrdOqlhR+usvE2UHD1o9FQAAbo0gAwB3EhNllSpJFy5INWtKP/1k9VQAALgtggwA3E3WrCbKnnwyLsp+/NHqqQAAcEsEGQC4oyxZpNBQ6amnpL//NlF24IDVUwEA4HYIMgBwVzFR9swz0sWLUq1a0g8/WD0VAABuhSADAHcWECCtXSs9+2xclO3fb/VUAAC4DYIMANxdTJRVrixdumSibN8+q6cCAMAtEGQAAMnfX1qzRqpSRbp8WapdW9q71+qpAABweQQZAMCIibKqVeOibM8eq6cCAMClEWQAgDh+ftLq1VK1alJEhImy3butngoAAJdFkAEA4ouJsueekyIjpTp1pF27rJ4KAACXRJABAO6VObP07bdS9epxUbZjh9VTAQDgcggyAEDCYqIsKEi6ckWqW1favt3qqQAAcCkEGQAgcZkySatWSTVqxEXZ999bPRUAAC6DIAMA3F9MlNWsKV29KtWrJ23bZvVUAAC4BIIMAPBgjzwirVxpThodE2Vbt1o9FQAATo8gAwAkTUyU1akjXbsm1a8vbdli9VQAADg1ggwAkHS+vtLy5VJwcFyUbd5s9VQAADgtggwAkDy+vtLXX5sFPq5fl154Qdq0yeqpAABwSgQZACD5YqKsXr24KNuwweqpAABwOgQZACBlfHykZctMjN24ITVsKK1fb/VUAAA4FYIMAJByPj7S0qVSgwZxUbZundVTAQDgNAgyAMDD8faWliwxMXbzptSokRQaavVUAAA4BYIMAPDwvL2lr74yMRYTZWvXWj0VAAAOjyADAKSOmChr0kSKijI/16yxeioAABwaQQYASD0ZM0qLFklNm5ooa9pUWr3a6qkAAHBYBBkAIHXFRFmzZnFR9u23Vk8FAIBDIsgAAKkvQwZp4UKpRQvp1i0TZ6tWWT0VAAAOhyADAKSNDBmk+fOlli1NlDVvLq1cafVUAAA4FIIMAJB2MmSQ5s2TWrWSbt82e8xWrLB6KgAAHAZBBgBIWzFR9uKLJspatpSWL7d6KgAAHAJBBgBIe15e0ty5Ups2cVG2bJnVUwEAYDmCDACQPry8pC++kNq1k+7ckVq3lpYutXoqAAAsRZABANKPl5cUEiK1bx8XZUuWWD0VAACWIcgAAOkrJso6dpTu3jXfLVu82OqpAACwhJfVAwAA3JCnpzR7tmSzSZ9/LrVtK9ntZo8ZAABuhCADAFjD01OaNctEWUiI+W6Z3W72mAEA4CY4ZBEAYB1PT2nmTKlLF3P4Yrt25mTSAAC4CYIMAGAtT0/ps8+krl2l6GipQwdz3jIAANwAQQYAsJ6Hh/Tpp1L37ibKOnY05y0DAMDFEWQAAMfg4SFNny716GGirHNnc94yAABcGEEGAHAcHh7SJ59IPXvGRVlIiNVTAQCQZggyAIBj8fCQpk2Tevc2qy526SLNmWP1VAAApAmCDADgeDw8pI8/lvr0MVHWtas5bxkAAC6GIAMAOCabTfroI6lvXxNl3bqZJfIBAHAhDhdkY8aM0VNPPSU/Pz/lzJlTTZs2VVhYWLxtjh07pmbNmilHjhzy9/dX69atde7cuXjb/Prrr2rSpImyZ88uf39/VatWTRs3brzva48aNUolS5ZUpkyZlDVrVtWuXVu7du1K9fcIAEgim02aMkV65RUTZd27myXyAQBwEQ4XZJs3b1bfvn21c+dOhYaG6vbt2woODta1a9ckSdeuXVNwcLBsNps2bNig77//Xrdu3VKjRo0UHR0d+zwNGzbUnTt3tGHDBu3bt0/ly5dXw4YNdfbs2URfu3jx4po6daoOHjyobdu2qVChQgoODtZff/2V5u8bAJAIm02aPFnq18/c7tFDmjHD2pkAAEglNrvdbrd6iPv566+/lDNnTm3evFnVq1fXd999p/r16+vSpUvy9/eXJEVERChr1qz67rvvVLt2bV24cEE5cuTQli1b9Nxzz0mSrly5In9/f4WGhqp27dpJeu3IyEgFBARo3bp1qlWrVpK3j4iIiJ0NAJBK7HZpwAATZ5JZjbFXL2tnApBmwsPDNWPGDPXs2VN58uSxehwg2ZLaBg63h+zfIiIiJEnZsmWTJEVFRclms8nb2zt2Gx8fH3l4eGjbtm2SpMDAQJUoUUKff/65rl27pjt37mj69OnKmTOnKlWqlKTXvXXrlmbMmKGAgACVL18+wW2ioqIUGRkZ7wIASCM2mzRpkokyyazCOG2atTMBAPCQHDrIoqOj1b9/f1WtWlVly5aVJD377LPKlCmT3nzzTV2/fl3Xrl3ToEGDdPfuXYWHh0uSbDab1q1bpx9++EF+fn7y8fHRxIkTtWbNGmXNmvW+r7lq1SplzpxZPj4+mjRpkkJDQ5U9e/YEtx0zZowCAgJiL/nz50/dDwAAEJ/NJn3wgfTaa+b2Sy+Z1RgBAHBSDh1kffv21aFDh7RgwYLY+3LkyKHFixdr5cqVypw5swICAnT58mVVrFhRHh7m7djtdvXt21c5c+bU1q1btXv3bjVt2lSNGjWKjbbE1KhRQwcOHND27dtVr149tW7dWufPn09w2yFDhigiIiL2curUqdR78wCAhNls0vjx0qBB5nbfvtLUqdbOBABACnlZPUBiXn75Za1atUpbtmxRvnz54v0uODhYx44d04ULF+Tl5aUsWbIod+7cKlKkiCRpw4YNWrVqVbzvmX388ccKDQ1VSEiIBg8enOjrZsqUScWKFVOxYsX07LPP6rHHHtPMmTM1ZMiQe7b19vaOd+gkACCd2GzS+++b85W9/37cKoyvvGL1ZAAAJIvDBZndbtcrr7yiZcuWadOmTSpcuHCi28YcSrhhwwadP39ejRs3liRdv35dkmL3mMXw8PCItxJjUkRHRysqKipZjwEApAObTRo71kTZ2LFmFcboaOnVV62eDACAJHO4Qxb79u2ruXPnat68efLz89PZs2d19uxZ3bhxI3ab2bNna+fOnTp27Jjmzp2rVq1aacCAASpRooQkqXLlysqaNas6d+6sH3/8Ub/++qtef/11HT9+XA0aNIh9npIlS2rZsmWSzHL6//nPf7Rz50798ccf2rdvn7p27arTp0+rVatW6fshAACSxmaT3ntPijmKoX9/s/AHAABOwuH2kE3734pZQUFB8e6fPXu2/u///k+SFBYWpiFDhujixYsqVKiQhg4dqgExq27J7Dlbs2aNhg4dqpo1a+r27dsqU6aMli9fHm/FxLCwsNhVHD09PXXkyBGFhITowoULCgwM1FNPPaWtW7eqTJkyafumAQApZ7NJ775r9pS9+640cKA5fHHgQKsnAwDggRz+PGTOhPOQAYCF7HZp5Ejp7bfN7QkT4lZjBOB0OA8ZnJ3LnIcMAIAksdmk0aOlESPM7UGDzGqMAAA4sGQfshgVFaVdu3bpjz/+0PXr15UjRw5VqFDhvotvAACQLmKizMNDGjVKeuMNs9DHm29aPRkAAAlKcpB9//33mjx5slauXKnbt28rICBAvr6+unjxoqKiolSkSBH17NlTvXv3lp+fX1rODADA/Y0caeJs5Ehp8GBzOON9TnkCAIBVknTIYuPGjfXiiy+qUKFC+u6773TlyhX9/fff+vPPP3X9+nX99ttvGjZsmNavX6/ixYsrNDQ0recGAOD+RoyQ3nrLXB8yxKzGCACAg0nSHrIGDRpoyZIlypAhQ4K/L1KkiIoUKaLOnTvrl19+UXh4eKoOCQBAigwfbg5fHDZMGjrUHL44bJjVUwEAECtJQdarV68kP2Hp0qVVunTpFA8EAECqGjrURNl//mMCzW43PwEAcACssggAcH1DhkhjxpjrI0aYhT8AAHAASV7Uo3DhwrLZbPfdxmaz6dixYw89FAAAqW7wYLOn7M03zQqMdrv5CQCAhZIcZP3790/0dydOnND06dMVFRWVGjMBAJA23njDRNnrr5u9ZDFR9oD/4AgAQFpJcpC9+uqr99x38eJFvf3225o2bZqeeeYZjRs3LlWHAwAg1Q0aZAJs0CCzCmN0tPlJlAEALJDsE0NL0o0bNzRx4kRNmDBBBQsW1NKlS/XCCy+k9mwAAKSN114ze8oGDpTeecdE2TvvEGUAgHSXrCC7e/euPv30U40ePVo+Pj7673//qw4dOjzwu2UAADicAQNMgA0YYM5RZrdL775LlAEA0lWSg2zRokUaNmyYLl++rKFDh6pPnz7KmDFjWs4GAEDa6t/f7Cl79VWzCmN0tPlJlAEA0kmSg6xNmzby9fVV27Zt9ccff2jw4MEJbjdx4sRUGw4AgDTXr5+JsldekcaNM3vKxo4lygAA6SLJQVa9evUHLmvPoYsAAKf08ssmwF5+WXr/fbOn7P33iTIAQJpLcpBt2rQpDccAAMBiffuaAOvbV5owwUTZhAlEGQAgTXkk9wGHDh1K9Hdff/31w8wCAIC1XnpJmjbNXJ840azGaLdbOxMAwKUlO8jq1q2r48eP33P/kiVL1L59+1QZCgAAy/TuLU2fbq5PmmRWYSTKAABpJNlB1r17d9WuXVtnz56NvW/hwoXq1KmT5syZk5qzAQBgjZ49pRkzzPXJk81qjEQZACANJPvE0KNHj9bFixdVu3ZtbdmyRWvWrFH37t31xRdfqEWLFmkxIwAA6a9HD7P6Yo8e0n//a75T9t//8p0yAECqSnaQSdKUKVPUvn17Pfvsszp9+rTmz5+vJk2apPZsAABYq1s3E2Ddu0tTp5oomzqVKAMApJokBdmKFSvuua958+baunWr2rZtK5vNFrtN48aNU3dCAACs1LWrCbBu3aSPPzaHLk6davaeAQDwkJIUZE2bNk30d7NmzdKsWbMkmfOQ3b17N1UGAwDAYXTpYgKsSxezCqPdLn30EVEGAHhoSQqy6OjotJ4DAADH1rmz2VP2f/8nffKJOXxx2jSiDADwUPhbBACApOrUSfr8cxNhM2ZIvXqZMAMAIIVStKjHnj17tHHjRp0/f/6evWcTJ05MlcEAAHBIHTqYPWWdOkmffWYOX5wxgz1lAIAUSXaQvffeexo2bJhKlCihXLlyyfaPlaZsrDoFAHAH7dubKOvYUZo50+wl++wzogwAkGzJDrLJkydr1qxZ+r//+780GAcAACfRrp0JsPbtpdmzzZ6yzz6TPD2tngwA4ESS/Z/yPDw8VLVq1bSYBQAA59KmjTRvnomwOXPM0visNgwASIZkB9mAAQP00UcfpcUsAAA4nxdflObPN1EWEmKWxifKAABJlOxDFgcNGqQGDRqoaNGiKl26tDJkyBDv90uXLk214QAAcAqtWpnvlLVpI33xhTl8cc4cDl8EADxQsveQ9evXTxs3blTx4sUVGBiogICAeBcAANxSy5bSwoWSl5c0d65ZhfHOHaunAgA4uGTvIQsJCdGSJUvUoEGDtJgHAADn1aKFtGiR1Lq1+W6Z3W7OW+aVorPMAADcQLL3kGXLlk1FixZNi1kAAHB+zZpJixebCJs/3yyNz54yAEAikh1ko0aN0siRI3X9+vW0mAcAAOfXtKn01VdShgzSggVmaXyiDACQgGQfQ/Hf//5Xx44dU65cuVSoUKF7FvXYv39/qg0HAIDTatJEWrIk7jBGu1368ksTaQAA/E+yg6xp06ZpMAYAAC6oUSNp6VITZYsXS9HR5jBGogwA8D/JDrKRI0emxRwAALimhg1NlDVvbvaYtWljDmMkygAASuJ3yOx2e1rPAQCA62rQQFq2TMqY0cTZiy9Kt25ZPRUAwAEkKcjKlCmjBQsW6NYD/vL47bff1KdPH40dOzZVhgMAwGW88IK0fLnk7W3irHVrogwAkLRDFqdMmaI333xTL730kurUqaMnn3xSefPmlY+Pjy5duqRffvlF27Zt088//6yXX35Zffr0Seu5AQBwPvXqmShr0sT8bNXKfLcsY0arJwMAWCRJQVarVi3t3btX27Zt08KFC/Xll1/qjz/+0I0bN5Q9e3ZVqFBBnTp1Uvv27ZU1a9a0nhkAAOdVt660YoWJshUrpJYtTZR5e1s9GQDAAsla1KNatWqqVq1aWs0CAIB7CA42Mda4sbRypVmFcckSogwA3FCyTwwNAABSQZ060qpVkq+v9M03ZhXGmzetngoAkM4IMgAArFKrVlyUffstUQYAboggAwDASjVrmj1kvr7S6tVSs2ZEGQC4EYIMAACr1ahh9pA98oi0Zo1Z8OPGDaunAgCkA4IMAABHEBQUF2XffUeUAYCbSHaQ1axZU6NHj77n/kuXLqlmzZqpMhQAAG7p+efNYYuZMkmhoWYVxuvXrZ4KAJCGkh1kmzZt0tSpU9W0aVNdu3Yt9v5bt25p8+bNqTocAABup3p1c9hi5szSunVEGQC4uBQdsrhu3TqdPXtWzz77rE6cOJHKIwEA4OaqVYuLsvXrpYYNpX/8R1AAgOtIUZDlyZNHmzdv1uOPP66nnnpKmzZtSuWxAABwc1WrSmvXSn5+0saNRBkAuKhkB5nNZpMkeXt7a968eXr11VdVr149ffzxx6k+HAAAbq1Klbgo27RJatCAKAMAF5PsILPb7fFuDxs2TF9++aU++OCDVBsKAAD8T+XKZtVFf39p82bphRekq1etngoAkEqSHWTHjx9X9uzZ493XokUL7dy5U7NmzUq1wQAAwP88+2xclG3ZQpQBgAtJdpAVLFhQHh73Pqxs2bLq3LlzqgwFAAD+5ZlnzFL4AQHS1q1S/frSlStWTwUAeEheSd2wa9euSdqOvWQAAKSRp582UVanjrRtm1Svnjlvmb+/1ZMBAFIoyUE2Z84cFSxYUBUqVLjne2QAACCdPPWUOT9ZnTrS9u0mytasIcoAwEklOcj69Omj+fPn6/jx4+rSpYs6dOigbNmypeVsAAAgIU8+ac5PVru2tGOHVLeuibKAAKsnAwAkU5K/Q/bRRx8pPDxcb7zxhlauXKn8+fOrdevWWrt2LXvMAABIbxUrmj1lWbNKO3eaKIuIsHoqAEAyJWtRD29vb7Vt21ahoaH65ZdfVKZMGb300ksqVKiQrrLaEwAA6atiRbOnLFs2adcuKThYunzZ6qkAAMmQ7FUWYx/o4SGbzSa73a67d++m5kwAACCpKlSIi7Ldu4kyAHAyyQqyqKgozZ8/X3Xq1FHx4sV18OBBTZ06VSdPnlTmzJnTakYAAHA/TzwhbdggBQZKe/aYBT8uXbJ6KgBAEiR5UY+XXnpJCxYsUP78+dW1a1fNnz//nhNEAwAAi5Qvb6KsVi1p714TZaGh5jtmAACHleQg++STT1SgQAEVKVJEmzdv1ubNmxPcbunSpak2HAAASIZy5UyU1awp7dtnVmEMDTWHMwIAHFKSg6xTp06y2WxpOQsAAHhYjz8ubdxoomz/frPHbN06czgjAMDhJOvE0AAAwAmULRsXZQcOmD1lRBkAOKQUr7KYVsaMGaOnnnpKfn5+ypkzp5o2baqwsLB429y8eVN9+/ZVYGCgMmfOrBYtWujcuXMJPt/ff/+tfPnyyWaz6fIDVp369ddf1aRJE2XPnl3+/v6qVq2aNm7cmFpvDQCA9FOmjImyXLlMlNWqJV24YPVUAIB/cbgg27x5s/r27audO3cqNDRUt2/fVnBwsK5duxa7zYABA7Ry5UotXrxYmzdv1pkzZ9S8efMEn69bt24qV65ckl67YcOGunPnjjZs2KB9+/apfPnyatiwoc6ePZsq7w0AgHRVunRclP34o9lj9tdfVk8FAPgHhwuyNWvW6P/+7/9UpkwZlS9fXnPmzNHJkye1b98+SVJERIRmzpypiRMnqmbNmqpUqZJmz56t7du3a+fOnfGea9q0abp8+bIGDRr0wNe9cOGCfvvtNw0ePFjlypXTY489prFjx+r69es6dOhQmrxXAADSXKlS0qZNUu7c0sGDZk8ZUQYADsPhguzfIiIiJEnZ/rdC1L59+3T79m3Vrl07dpuSJUuqQIEC2rFjR+x9v/zyi9566y19/vnn8vB48NsMDAxUiRIl9Pnnn+vatWu6c+eOpk+frpw5c6pSpUoJPiYqKkqRkZHxLgAAOJySJU2U5cljoqxmTen8eaunAgDIwYMsOjpa/fv3V9WqVVW2bFlJ0tmzZ5UxY0ZlyZIl3ra5cuWKPbQwKipKbdu21fjx41WgQIEkvZbNZtO6dev0ww8/yM/PTz4+Ppo4caLWrFmjrImcw2XMmDEKCAiIveTPnz/lbxYAgLRUooSJsrx5pUOHpBo1pES+fw0ASD8OHWR9+/bVoUOHtGDBgmQ9bsiQISpVqpQ6dOiQ5MfY7Xb17dtXOXPm1NatW7V79241bdpUjRo1Unh4eKKvExEREXs5depUsuYEACBdFS9uouzRR6VffjF7yogyALCUwwbZyy+/rFWrVmnjxo3Kly9f7P25c+fWrVu37lkx8dy5c8qdO7ckacOGDVq8eLG8vLzk5eWlWrVqSZKyZ8+ukSNHJvh6GzZs0KpVq7RgwQJVrVpVFStW1McffyxfX1+FhIQk+Bhvb2/5+/vHuwAA4NAeeyx+lNWoIbF4FQBYJsnnIUsvdrtdr7zyipYtW6ZNmzapcOHC8X5fqVIlZciQQevXr1eLFi0kSWFhYTp58qQqV64sSVqyZIlu3LgR+5g9e/aoa9eu2rp1q4oWLZrg616/fl2S7vm+mYeHh6Kjo1Pt/QEAYLlixUyU1aghHT5sfm7YYL5jBgBIVw4XZH379tW8efO0fPly+fn5xX4vLCAgQL6+vgoICFC3bt00cOBAZcuWTf7+/nrllVdUuXJlPfvss5J0T3Rd+N95V0qVKhX73bPdu3erU6dOWr9+vR599FFVrlxZWbNmVefOnTVixAj5+vrq008/1fHjx9WgQYP0+wAAAEgP/4yyI0fMz40biTIASGcOd8jitGnTFBERoaCgIOXJkyf2snDhwthtJk2apIYNG6pFixaqXr26cufOraVLlybrda5fv66wsDDdvn1bkjmccc2aNbp69apq1qypJ598Utu2bdPy5ctVvnz5VH2PAAA4hKJFTZQVKCCFhUlBQdKZM1ZPBQBuxWa32+1WD+EqIiMjFRAQoIiICL5PBgBwHsePmz1kf/xhvmO2caP5jhlgofDwcM2YMUM9e/ZUHvbcwgkltQ0cbg8ZAABIZ4ULmz1lBQtKv/1m9pT9+afVUwGAWyDIAACAVKiQtHmz+Xn0qIkyTucCAGmOIAMAAEbBgmZPWeHC0rFjJspOnrR6KgBwaQQZAACIExNlRYpIv/9OlAFAGiPIAABAfAUKmCgrWtQs+BEUZBb8AACkOoIMAADcK3/+e6PsxAmLhwIA10OQAQCAhOXLZxb6eOwxE2NEGQCkOoIMAAAk7tFHzXnJHnvMHLb4/PNmjxkAIFUQZAAA4P4efdQcvli8uFng4/nnzYIfAICHRpABAIAHy5vXRFmJEub8ZM8/b5bGBwA8FIIMAAAkTZ48JspKlpT+/NN8p+zoUaunAgCnRpABAICky53bfKesVKm4KPvtN6unAgCnRZABAIDkiYmy0qWl06dNlP36q9VTAYBTIsgAAEDy5cploqxMGenMGRNlYWFWTwUATocgAwAAKZMzp7Rhg1S2rBQeLtWoQZQBQDIRZAAAIOViouzxx02UBQVJR45YPRUAOA2CDAAAPJwcOUyUlSsnnT1rouzwYaunAgCnQJABAICHlz27tH69VL68dO6cibJffrF6KgBweAQZAABIHTFR9sQT0vnz5jtlP/9s9VQA4NAIMgAAkHoCA02UVagQF2WHDlk9FQA4LIIMAACkrmzZpHXrpIoVpb/+MlF28KDVUwGAQyLIAABA6ouJskqVpAsXpJo1pZ9+snoqAHA4BBkAAEgbWbOaKHvyybgo+/FHq6cCAIdCkAEAgLSTJYsUGio99ZT0998myg4csHoqAHAYBBkAAEhbMVH2zDPSxYtSrVrSDz9YPRUAOASCDAAApL2AAGntWunZZ+OibP9+q6cCAMsRZAAAIH3ERFnlytKlSybK9u2zeioAsBRBBgAA0o+/v7RmjVSlinT5slS7trR3r9VTAYBlCDIAAJC+YqKsatW4KNuzx+qpAMASBBkAAEh/fn7S6tVStWpSRISJst27rZ4KANIdQQYAAKwRE2XPPSdFRkp16ki7dlk9FQCkK4IMAABYJ3Nm6dtvperV46Jsxw6rpwKAdEOQAQAAa8VEWVCQdOWKVLeutH271VMBQLogyAAAgPUyZZJWrZJq1IiLsu+/t3oqAEhzBBkAAHAMMVFWs6Z09apUr560bZvVUwFAmiLIAACA43jkEWnlSnPS6Jgo27rV6qkAIM0QZAAAwLHERFmdOtK1a1L9+tKWLVZPBQBpgiADAACOx9dXWr5cCg6Oi7LNm62eCgBSHUEGAAAck6+v9PXXZoGP69elF16QNm2yeioASFUEGQAAcFwxUVavXlyUbdhg9VQAkGoIMgAA4Nh8fKRly0yM3bghNWworV9v9VQAkCoIMgAA4Ph8fKSlS6UGDeKibN06q6cCgIdGkAEAAOfg7S0tWWJi7OZNqVEjKTTU6qkA4KEQZAAAwHl4e0tffWViLCbK1q61eioASDGCDAAAOJeYKGvSRIqKMj/XrLF6KgBIEYIMAAA4n4wZpUWLpKZNTZQ1bSqtXm31VACQbAQZAABwTjFR1qxZXJR9+63VUwFAshBkAADAeWXIIC1cKLVoId26ZeJs1SqrpwKAJCPIAACAc8uQQZo/X2rZ0kRZ8+bSypVWTwUASUKQAQAA55chgzRvntSqlXT7ttljtmKF1VMBwAMRZAAAwDXERNmLL5ooa9lSWr7c6qkA4L4IMgAA4Dq8vKS5c6U2beKibNkyq6cCgEQRZAAAwLV4eUlffCG1ayfduSO1bi0tXWr1VACQIIIMAAC4Hi8vKSREat8+LsqWLLF6KgC4B0EGAABcU0yUdewo3b1rvlu2eLHVUwFAPF5WDwAAAJBmPD2l2bMlm036/HOpbVvJbjd7zADAARBkAADAtXl6SrNmmSgLCTHfLbPbzR4zALAYhywCAADX5+kpzZwpdeliDl9s186cTBoALEaQAQAA9+DpKX32mdS1qxQdLXXoYM5bBgAWIsgAAID78PCQPv1U6t7dRFnHjua8ZQBgEYIMAAC4Fw8Pafp0qUcPE2WdO5vzlgGABQgyAADgfjw8pE8+kXr1iouykBCrpwLghggyAADgnjw8pI8/lnr3NqsudukizZlj9VQA3AxBBgAA3FdMlL30komyrl3NecsAIJ0QZAAAwL3ZbNLUqVLfvibKunUzS+QDQDogyAAAAGw2acoU6ZVXTJR1726WyAeANOZwQbZlyxY1atRIefPmlc1m09dffx3v90uXLlVwcLACAwNls9l04MCBe55jxowZCgoKkr+/v2w2my5fvvzA1502bZrKlSsnf39/+fv7q3Llylq9enXqvCkAAOD4bDZp8mSpXz9zu0cPacYMa2cC4PIcLsiuXbum8uXL66OPPkr099WqVdO4ceMSfY7r16+rXr16+s9//pPk182XL5/Gjh2rffv2ae/evapZs6aaNGmin3/+OdnvAQAAOCmbTfrwQ+nVV83tXr3MEvkAkEa8rB7g3+rXr6/69esn+vuOHTtKkk6cOJHoNv3795ckbdq0Kcmv26hRo3i33333XU2bNk07d+5UmTJlkvw8AADAydls0qRJZsGPSZPMKozR0VKfPlZPBsAFOVyQOYK7d+9q8eLFunbtmipXrpzodlFRUYqKioq9HRkZmR7jAQCAtGazSR98YKLsgw/iVmF86SWrJwPgYhzukEUrHTx4UJkzZ5a3t7d69+6tZcuWqXTp0oluP2bMGAUEBMRe8ufPn47TAgCANGWzSePHS4MGmdt9+5rVGAEgFRFk/1CiRAkdOHBAu3btUp8+fdS5c2f98ssviW4/ZMgQRURExF5OnTqVjtMCAIA0Z7NJ778vvfGGuf3KK2Y1RgBIJRyy+A8ZM2ZUsWLFJEmVKlXSnj17NHnyZE1P5Mu83t7e8vb2Ts8RAQBAerPZpLFjzeGLY8eaVRijo+MW/gCAh8AesvuIjo6O9x0xAADgpmw26b33pCFDzO3+/c2CHwDwkBxuD9nVq1d19OjR2NvHjx/XgQMHlC1bNhUoUEAXL17UyZMndebMGUlSWFiYJCl37tzKnTu3JOns2bM6e/Zs7PMcPHhQfn5+KlCggLJlyyZJqlWrlpo1a6aXX35Zkjn8sH79+ipQoICuXLmiefPmadOmTVq7dm26vXcAAODAbDbp3XfNnrJ335UGDjQLfQwcaPVkAJyYw+0h27t3rypUqKAKFSpIkgYOHKgKFSpoxIgRkqQVK1aoQoUKatCggSSpTZs2qlChgj755JPY5/jkk09UoUIF9ejRQ5JUvXp1VahQQStWrIjd5tixY7pw4ULs7fPnz6tTp04qUaKEatWqpT179mjt2rWqU6dOmr9nAADgJGw26e23peHDze3XXjOrMAJACtnsdrvd6iFcRWRkpAICAhQRESF/f3+rxwEAAGnFbpdGjZLeesvcfv996fXXLR3J1YSHh2vGjBnq2bOn8uTJY/U4QLIltQ0cbg8ZAACAw7PZpNGjTZRJZhXGceMsHQmAcyLIAAAAUmrkSBNmkjR4sFmFEQCSgSADAAB4GCNGxB26OGSIWY0RAJKIIAMAAHhYw4dL77xjrg8dGncdAB6AIAMAAEgNQ4fG7R0bPtysxggAD0CQAQAApJYhQ6QxY8z1ESPivl8GAIkgyAAAAFLT4MFxKy6OGhW3EiMAJIAgAwAASG1vvCGNH2+ujx5tVmPk1K8AEkCQAQAApIVBg6QJE8z1t94yhzASZQD+hSADAABIK6+9Jk2caK6/8440bBhRBiAeggwAACAtDRggTZpkrr/3nlmNkSgD8D8EGQAAQFrr31+aPNlcHzPGrMZIlAEQQQYAAJA++vWTpkwx18eNk958kygDQJABAACkm5dflqZONdfHjzerMRJlgFsjyAAAANJT377SRx+Z6xMmmNUYiTLAbRFkAAAA6e2ll6Rp08z1iROlgQOJMsBNEWQAAABW6N1bmj7dXP/wQ7MaI1EGuB2CDAAAwCo9e0ozZpjrkyeb1RiJMsCtEGQAAABW6tFD+uwzyWaT/vtfsxojUQa4DYIMAADAat26xUXZ1KlmNUaiDHALBBkAAIAj6NpVmjnTRNnHH5vVGKOjrZ4KQBojyAAAABxFly7S7NkmyqZNI8oAN0CQAQAAOJLOnaU5c0yUffKJ1KcPUQa4MIIMAADA0XTqJH3+ueThYVZh7NWLKANcFEEGAADgiDp0iIuyzz4zS+QTZYDLIcgAAAAcVfv20hdfmCibOVPq3p0oA1wMQQYAAODI2rWTvvzSRNns2WaJ/Lt3rZ4KQCohyAAAABxdmzbSvHmSp6dZ8IMoA1wGQQYAAOAMXnxRmj/fRFlIiFkinygDnJ6X1QMAAAAgiVq1Msvht2ljvltmt5s9Zp6eVk8GIIXYQwYAAOBMWraUFi6UvLykuXPNEvl37lg9FYAUIsgAAACcTYsW0qJFJsrmzSPKACdGkAEAADijZs2kxYtNlM2fL3XsSJQBToggAwAAcFZNm0pffSVlyCAtWGDOW0aUAU6FIAMAAHBmTZpIS5aYKFu0yJy37PZtq6cCkEQEGQAAgLNr1EhaulTKmNEcxti2LVEGOAmCDAAAwBU0bBgXZUuWmKXxiTLA4RFkAAAArqJBA2nZMhNlS5eak0nfumX1VADugyADAABwJS+8IC1fLnl7mzhr3ZooAxwYQQYAAOBq6tWLi7Lly6VWrYgywEERZAAAAK6obl1pxQrJx8f8bNlSioqyeioA/0KQAQAAuKrg4LgoW7lSatGCKAMcDEEGAADgyurUkVatknx9pW++kZo3l27etHoqAP9DkAEAALi6WrXiouzbb4kywIEQZAAAAO6gZk2zh8zXV1q9WmrWjCgDHABBBgAA4C5q1DB7yB55RFqzRmrSRLpxw+qpALdGkAEAALiToKC4KPvuO6IMsBhBBgAA4G6ef94ctpgpkxQaKjVuLF2/bvVUgFsiyAAAANxR9ermsMXMmaV164gywCIEGQAAgLuqVi0uytavlxo2lK5ds3oqwK0QZAAAAO6salVp7VrJz0/auJEoA9IZQQYAAODuqlSJi7JNm6QGDYgyIJ0QZAAAAJAqVzarLvr7S5s3Sy+8IF29avVUgMsjyAAAAGA8+2xclG3ZQpQB6YAgAwAAQJxnnjFL4QcESFu3SvXrS1euWD0V4LIIMgAAAMT39NNxUbZtm1SvnhQZafVUgEsiyAAAAHCvp54y5yfLkkXavp0oA9IIQQYAAICEPfmkOT9Z1qzSjh1S3bpSRITVUwEuhSADAABA4ipWNHvKsmaVdu4kyoBURpABAADg/ipWNHvKsmWTdu2SgoOly5etngpwCQQZAAAAHqxCBWnDBikwUNq9mygDUglBBgAAgKQpX97sKQsMlPbskerUkS5dsnoqwKkRZAAAAEi68uXNnrLs2aW9e4ky4CERZAAAAEiecuWkjRulHDmkffuk2rWlixetngpwSgQZAAAAkq9sWbOnLEcOaf9+qVYt6e+/rZ4KcDoEGQAAAFKmbFmzpyxnTunAAbOnjCgDkoUgAwAAQMqVKWOiLFcuE2W1akkXLlg9FeA0nDLItmzZokaNGilv3ryy2Wz6+uuv4/3eZrMleBk/fnyiz3n37l0NHz5chQsXlq+vr4oWLaq3335bdrs9jd8NAACAkytdOi7KfvxRqllT+usvq6cCnIJTBtm1a9dUvnx5ffTRRwn+Pjw8PN5l1qxZstlsatGiRaLPOW7cOE2bNk1Tp07V4cOHNW7cOL3//vuaMmVKWr0NAAAA11GqlLRpk5Q7t3TwoNlTRpQBD+Rl9QApUb9+fdWvXz/R3+fOnTve7eXLl6tGjRoqUqRIoo/Zvn27mjRpogYNGkiSChUqpPnz52v37t2pMzQAAICrK1nSRFmNGibKatY05y3LmdPqyQCH5ZR7yJLj3Llz+uabb9StW7f7blelShWtX79ev/76qyTpxx9/1LZt2+4bflFRUYqMjIx3AQAAcGslSpgoy5tXOnTIxNm5c1ZPBTgslw+ykJAQ+fn5qXnz5vfdbvDgwWrTpo1KliypDBkyqEKFCurfv7/at2+f6GPGjBmjgICA2Ev+/PlTe3wAAADnU7y4ibJHH5V++cXsKSPKgAS5fJDNmjVL7du3l4+Pz323W7Rokb788kvNmzdP+/fvV0hIiCZMmKCQkJBEHzNkyBBFRETEXk6dOpXa4wMAADinxx6LH2U1akhnz1o9FeBwnPI7ZEm1detWhYWFaeHChQ/c9vXXX4/dSyZJjz/+uP744w+NGTNGnTt3TvAx3t7e8vb2TtWZAQAAXEaxYnHfKTt82PzcsEHKk8fqyQCH4dJ7yGbOnKlKlSqpfPnyD9z2+vXr8vCI/3F4enoqOjo6rcYDAABwfTFRlj+/dOSIibLwcKunAhyGUwbZ1atXdeDAAR04cECSdPz4cR04cEAnT56M3SYyMlKLFy9W9+7dE3yOWrVqaerUqbG3GzVqpHfffVfffPONTpw4oWXLlmnixIlq1qxZmr4XAAAAl1e0qImyAgWksDApKEg6c8bqqQCH4JSHLO7du1c1atSIvT1w4EBJUufOnTVnzhxJ0oIFC2S329W2bdsEn+PYsWO68I+zyE+ZMkXDhw/XSy+9pPPnzytv3rzq1auXRowYkXZvBAAAwF0UKRJ3+OKvv5oo27jRfMcMcGM2u91ut3oIVxEZGamAgABFRETI39/f6nEAAAAcz4kTJsb++MMczrhxo5Qv3z2bhYeHa8aMGerZs6fy8J0zOKGktoFTHrIIAAAAJ1WokLR5s/l59KiJM1aqhhsjyAAAAJC+ChY0hy8WLiwdO2ai7B9rAQDuhCADAABA+ouJsiJFpN9/J8rgtggyAAAAWKNAARNlRYtKx4/HfbcMcCMEGQAAAKyTP/+9UXbihMVDAemHIAMAAIC18uUzC3089ljcKoycpwxugiADAACA9R591CyB/9hj5rDFHj2snghIF055YmhHd/bsWV27ds3qMQAAAJyLh4f01VdSz566cP26JOnCzz9bPBSQMleuXEnSdgRZGpg9e7Z8fHysHgMAAMA51a8fe3Xp999L339v4TBAyty8eTNJ29nsdrs9jWdxGzFn4w4LC5Ofn5/V4wAAADitC7/9pqUbN6r5kiXK7uEhzZhhFgABnMSVK1dUokQJRUREyN/fP9Ht2EOWBnLnzn3fDx0AAABJsHGjsvv5Kc/27VLz5nHfMQOcQKZMmZK0HYt6AAAAwHFNny6VLi2dPm1WX/z1V6snAlIVQQYAAADHFRho9oyVKWOWwg8KksLCrJ4KSDUEGQAAABxbzpzShg1S2bJSeLhUo4Z05IjVUwGpgiADAACA44uJsscfJ8rgUggyAAAAOIccOUyUlSsnnT1rDl88fNjqqYCHQpABAADAeWTPLq1fL5UvL507Z6Lsl1+sngpIMYIMAAAAziUmyp54Qjp/3kTZzz9bPRWQIgQZAAAAnE9goImyChWkv/4y3yk7dMjqqYBkI8gAAADgnLJlk9atkypWjIuygwetngpIFoIMAAAAzismyipVki5ckGrWlH76yeqpgCQjyAAAAODcsmY1Ufbkk3FR9uOPVk8FJAlBBgAAAOeXJYsUGio99ZT0998myg4csHoq4IEIMgAAALiGmCh75hnp4kWpVi3phx+sngq4L4IMAAAAriMgQFq7Vnr22bgo27/f6qmARBFkAAAAcC0xUVa5snTpkomyffusngpIEEEGAAAA1+PvL61ZI1WpIl2+LNWuLe3da/VUwD0IMgAAALimmCirWjUuyvbssXoqIB6CDAAAAK7Lz09avVqqVk2KiDBRtnu31VMBsQgyAAAAuLaYKHvuOSkyUqpTR9q1y+qpAEkEGQAAANxB5szSt99K1avHRdmOHVZPBRBkAAAAcBMxURYUJF25ItWtK23fbvVUcHMEGQAAANxHpkzSqlVSjRpxUfb991ZPBTdGkAEAAMC9xERZzZrS1atSvXrStm1WTwU3RZABAADA/TzyiLRypTlpdEyUbd1q9VRwQwQZAAAA3FNMlNWpI127JtWvL23ZYvVUcDMEGQAAANyXr6+0fLkUHBwXZZs3Wz0V3AhBBgAAAPfm6yt9/bVZ4OP6demFF6RNm6yeCm6CIAMAAABioqxevbgo27DB6qngBggyAAAAQJJ8fKRly0yM3bghNWworV9v9VRwcQQZAAAAEMPHR1q6VGrQIC7K1q2zeiq4MIIMAAAA+Cdvb2nJEhNjN29KjRpJoaFWTwUXRZABAAAA/+btLX31lYmxmChbu9bqqeCCCDIAAAAgITFR1qSJFBVlfq5ZY/VUcDEEGQAAAJCYjBmlRYukpk1NlDVtKq1ebfVUcCEEGQAAAHA/MVHWrFlclH37rdVTwUUQZAAAAMCDZMggLVwotWgh3bpl4mzVKqunggsgyAAAAICkyJBBmj9fatnSRFnz5tLKlVZPBSdHkAEAAABJlSGDNG+e1KqVdPu22WO2YoXVU8GJEWQAAABAcsRE2Ysvmihr2VJavtzqqeCkCDIAAAAguby8pLlzpTZt4qJs2TKrp4ITIsgAAACAlPDykr74QmrXTrpzR2rdWlq61Oqp4GQIMgAAACClvLykkBCpffu4KFuyxOqp4EQIMgAAAOBhxERZx47S3bvmu2WLF1s9FZyEl9UDAAAAAE7P01OaPVuy2aTPP5fatpXsdrPHDLgPggwAAABIDZ6e0qxZJspCQsx3y+x2s8cMSASHLAIAAACpxdNTmjlT6tLFHL7Yrp05mTSQCIIMAAAASE2entJnn0ldu0rR0VKHDua8ZUACCDIAAAAgtXl4SJ9+KnXvbqKsY0dz3jLgXwgyAAAAIC14eEjTp0s9epgo69zZnLcM+AeCDAAAAEgrHh7SJ59IvXrFRVlIiNVTwYEQZAAAAEBa8vCQPv5Y6t3brLrYpYs0Z47VU8FBEGQAAABAWouJspdeMlHWtas5bxncHkEGAAAApAebTZo6Verb10RZt25miXy4NYIMAAAASC82mzRlivTKKybKunc3S+TDbRFkAAAAQHqy2aTJk6VXXzW3e/SQZsywdiZYhiADAAAA0pvNJk2aJPXvb2736mWWyIfbcbogs9lsCV7Gjx8fu02hQoXu+f3YsWMTfc4TJ04k+ryLFy9Oj7cFAAAAd2OzSRMnSgMGmNu9e0vTplk7E9Kdl9UDJFd4eHi826tXr1a3bt3UokWLePe/9dZb6tGjR+xtPz+/RJ8zf/789zzvjBkzNH78eNWvXz8VpgYAAAASYLNJH3xgVmH84IO4VRhfesnqyZBOnC7IcufOHe/28uXLVaNGDRUpUiTe/X5+fvdsmxhPT897tl22bJlat26tzJkzP9zAAAAAwP3YbNL48SbKxo83qzBGR0svv2z1ZEgHTnfI4j+dO3dO33zzjbp163bP78aOHavAwEBVqFBB48eP1507d5L8vPv27dOBAwcSfN5/ioqKUmRkZLwLAAAAkGw2mzRunPTGG+b2K6+Y1Rjh8pxuD9k/hYSEyM/PT82bN493f79+/VSxYkVly5ZN27dv15AhQxQeHq6JEycm6XlnzpypUqVKqUqVKvfdbsyYMRo9enSK5wcAAABi2WzS2LFmT9nYsVK/fmZPWcxqjHBJDr2H7Msvv1TmzJljL1u3bo33+1mzZql9+/by8fGJd//AgQMVFBSkcuXKqXfv3vrggw80ZcoURUVFPfA1b9y4oXnz5j1w75gkDRkyRBEREbGXU6dOJe8NAgAAAP9ks0nvvScNGWJu9+9vVmOEy3LoPWSNGzfWM888E3v70Ucfjb2+detWhYWFaeHChQ98nmeeeUZ37tzRiRMnVKJEiftu+9VXX+n69evq1KnTA5/X29tb3t7eD9wOAAAASDKbTXr3XbOn7N13pYEDzUIfAwdaPRnSgEMHmZ+fX6KrI86cOVOVKlVS+fLlH/g8Bw4ckIeHh3LmzPnAbWfOnKnGjRsrR44cyZ4XAAAASBU2m/T22ybK3n5beu01E2WvvWb1ZEhlDn3IYmIiIyO1ePFide/e/Z7f7dixQx9++KF+/PFH/f777/ryyy81YMAAdejQQVmzZpUknT59WiVLltTu3bvjPfbo0aPasmVLgs8LAAAApCubTRo9WhoxwtweNMiswgiX4tB7yBKzYMEC2e12tW3b9p7feXt7a8GCBRo1apSioqJUuHBhDRgwQAP/sYv39u3bCgsL0/Xr1+M9dtasWcqXL5+Cg4PT/D0AAAAADxQTZR4e0qhRZhXG6GjpzTetngypxGa32+1WD+EqIiMjFRAQoIiICPn7+1s9DgAAgNMKDw/XjBkz1LNnT+XJk8fqcRzDW29JI0ea62PGSIMHWzsP7iupbeCUhywCAAAAbmfECBNlklmF8b33rJ0HqYIgAwAAAJzF8OHSO++Y60OHxl2H0yLIAAAAAGcydGjc3rHhw80qjHBaBBkAAADgbIYMMd8jk8yhjKNHWzsPUowgAwAAAJzR4MHSuHHm+qhR5gKnQ5ABAAAAzuqNN+LOTTZ6tFmFkUXUnQpBBgAAADizQYOkCRPM9bfeMocwEmVOgyADAAAAnN1rr0kTJ5rr77wjDRtGlDkJggwAAABwBQMGSJMmmevvvWdWYyTKHB5BBgAAALiK/v2lyZPN9TFjzGqMRJlDI8gAAAAAV9KvnzRlirk+bpz05ptEmQMjyAAAAABX8/LL0tSp5vr48WY1RqLMIRFkAAAAgCvq21f66CNzfcIEsxojUeZwCDIAAADAVb30kjRtmrk+caI0cCBR5mAIMgAAAMCV9e4tTZ9urn/4oVmNkShzGAQZAAAA4Op69pRmzDDXJ0+WXn2VKHMQBBkAAADgDnr0kD77TLLZzCqM/foRZQ6AIAMAAADcRbducVE2dapZjZEosxRBBgAAALiTrl2lmTNNlH38sVmNMTra6qncFkEGAAAAuJsuXaTZs02UTZtmVmMkyixBkAEAAADuqHNnac4cE2XTp0t9+hBlFiDIAAAAAHfVqZP0+eeSh4dZhbFXL6IsnRFkAAAAgDvr0CEuyj77zCyRT5SlG4IMAAAAcHft20tffGGibOZMqXt3oiydEGQAAAAApHbtpC+/NFE2e7ZZIv/uXauncnkEGQAAAACjTRtp3jzJ09Ms+EGUpTmCDAAAAECcF1+U5s83URYSYpbIJ8rSjJfVAwAAAABwMK1ameXw27Qx3y2z280eM09PqydzOewhAwAAAHCvli2lhQslLy9p7lyzRP6dO1ZP5XIIMgAAAAAJa9FCWrTIRNm8eURZGiDIAAAAACSuWTNp8WITZfPnSx07EmWpiCADAAAAcH9Nm0pffSVlyCAtWGDOW0aUpQqCDAAAAMCDNWkiLVliomzRInPestu3rZ7K6RFkAAAAAJKmUSNp6VIpY0ZzGGPbtkTZQyLIAAAAACRdw4ZxUbZkiVkanyhLMYIMAAAAQPI0aCAtW2aibOlSczLpW7esnsopEWQAAAAAku+FF6TlyyVvbxNnrVsTZSlAkAEAAABImXr14qJs+XKpVSuiLJkIMgAAAAApV7eutGKF5ONjfrZsKUVFWT2V0yDIAAAAADyc4OC4KFu5UmrRgihLIoIMAAAAwMOrU0datUry9ZW++UZq3ly6edPqqRweQQYAAAAgddSqFRdl335LlCUBQQYAAAAg9dSsafaQ+fpKq1dLzZoRZfdBkAEAAABIXTVqmD1kjzwirVkjNWki3bhh9VQOiSADAAAAkPqCguKi7LvviLJEEGQAAAAA0sbzz5vDFjNlkkJDpcaNpevXrZ7KoRBkAAAAANJO9ermsMXMmaV164iyfyHIAAAAAKStatXiomz9eqlhQ+naNauncggEGQAAAIC0V7WqtHat5OcnbdxIlP0PQQYAAAAgfVSpEhdlmzZJDRq4fZQRZAAAAADST+XKZtVFf39p82bphRekq1etnsoyBBkAAACA9PXss3FRtmWLW0cZQQYAAAAg/T3zjFkKPyBA2rpVql9funLF6qnSHUEGAAAAwBpPPx0XZdu2SfXqSZGRVk+VrggyAAAAANZ56ilzfrIsWaTt290uyggyAAAAANZ68klzfrKsWaUdO6S6daWICKunShcEGQAAAADrVaxo9pRlzSrt3Ok2UUaQAQAAAHAMFSuaPWXZskm7dknBwdLly1ZPlaYIMgAAAACOo0IFacMGKTBQ2r3b5aOMIAMAAADgWMqXN3vKAgOlPXukOnWkS5esnipNEGQAAAAAHE/58mZPWfbs0t69LhtlBBkAAAAAx1SunLRxo5Qjh7Rvn1S7tnTxotVTpSqCDAAAAIDjKlvW7CnLkUPav1+qVUv6+2+rp0o1BBkAAAAAx1a2rNlTljOndOCA2VPmIlFGkAEAAABwfGXKmCjLlctEWa1a0oULVk/10AgyAAAAAM6hdOm4KPvxR6lmTemvv6ye6qEQZAAAAACcR6lS0qZNUu7c0sGDZk+ZE0eZQwXZ0qVLFRwcrMDAQNlsNh04cCDB7Xbs2KGaNWsqU6ZM8vf3V/Xq1XXjxo3Y3+/fv1916tRRlixZFBgYqJ49e+rq1auJvu7t27f15ptv6vHHH1emTJmUN29ederUSWfOnEnttwgAAADgYZUsaaIsTx4TZTVrSufPWz1VijhUkF27dk3VqlXTuHHjEt1mx44dqlevnoKDg7V7927t2bNHL7/8sjw8zFs5c+aMateurWLFimnXrl1as2aNfv75Z/3f//1fos95/fp17d+/X8OHD9f+/fu1dOlShYWFqXHjxqn9FgEAAACkhhIlTJTlzSsdOiTVqCGdO2f1VMnmZfUA/9SxY0dJ0okTJxLdZsCAAerXr58GDx4ce1+JEiVir69atUoZMmTQRx99FBtpn3zyicqVK6ejR4+qWLFi9zxnQECAQkND4903depUPf300zp58qQKFCjwMG8LAAAAQFooXtxEWY0a0i+/mD1lGzaY75g5CYfaQ/Yg58+f165du5QzZ05VqVJFuXLl0vPPP69t27bFbhMVFaWMGTPGxpgk+fr6SlK87R4kIiJCNptNWbJkSXSbqKgoRUZGxrsAAAAASEePPWaiLF8+E2W1akm3blk9VZI5VZD9/vvvkqRRo0apR48eWrNmjSpWrKhatWrpt99+kyTVrFlTZ8+e1fjx43Xr1i1dunQpdm9aeHh4kl7n5s2bevPNN9W2bVv5+/snut2YMWMUEBAQe8mfP/9DvkMAAAAAyVasmImyAgWk116TMma0eqIksyzIvvzyS2XOnDn2snXr1gc+Jjo6WpLUq1cvdenSRRUqVNCkSZNUokQJzZo1S5JUpkwZhYSE6IMPPtAjjzyi3Llzq3DhwsqVK1e8vWaJuX37tlq3bi273a5p06bdd9shQ4YoIiIi9nLq1KkkvHMAAAAAqa5oUenwYalLF6snSRbLvkPWuHFjPfPMM7G3H3300Qc+Jk+ePJKk0qVLx7u/VKlSOnnyZOztdu3aqV27djp37pwyZcokm82miRMnqkiRIvd9/pgY++OPP7Rhw4b77h2TJG9vb3l7ez9wbgAAAADp4JFHrJ4g2SwLMj8/P/n5+SXrMYUKFVLevHkVFhYW7/5ff/1V9evXv2f7XP/7Mt+sWbPk4+OjOnXqJPrcMTH222+/aePGjQoMDEzWbAAAAACQXA61yuLFixd18uTJ2PN/xYRX7ty5lTt3btlsNr3++usaOXKkypcvryeeeEIhISE6cuSIvvrqq9jnmTp1qqpUqaLMmTMrNDRUr7/+usaOHRtvgY6SJUtqzJgxatasmW7fvq2WLVtq//79WrVqle7evauzZ89KkrJly6aMTnQMKgAAAADn4VBBtmLFCnX5xzGfbdq0kSSNHDlSo0aNkiT1799fN2/e1IABA3Tx4kWVL19eoaGhKlq0aOzjdu/erZEjR+rq1asqWbKkpk+fHrukfoywsDBFRERIkk6fPq0VK1ZIkp544ol4223cuFFBQUGp/E4BAAAAQLLZ7Xa71UO4isjISAUEBCgiIuKB3z8DAABA4sLDwzVjxgz17Nkzdh0BwJkktQ2catl7AAAAAHAlBBkAAAAAWIQgAwAAAACLEGQAAAAAYBGCDAAAAAAsQpABAAAAgEUIMgAAAACwCEEGAAAAABYhyAAAAADAIgQZAAAAAFiEIAMAAAAAixBkAAAAAGARggwAAAAALEKQAQAAAIBFCDIAAAAAsAhBBgAAAAAWIcgAAAAAwCIEGQAAAABYhCADAAAAAIsQZAAAAABgEYIMAAAAACxCkAEAAACARQgyAAAAALAIQQYAAAAAFiHIAAAAAMAiBBkAAAAAWIQgAwAAAACLeFk9gCux2+2SpMjISIsnAQAAcG5XrlzRzZs3deXKFWXKlMnqcYBki2mCmEZIjM3+oC2QZH/++afy589v9RgAAAAAHMSpU6eUL1++RH9PkKWi6OhonTlzRn5+frLZbJbOEhkZqfz58+vUqVPy9/e3dBZnwueWcnx2KcPnljJ8binHZ5cyfG4pw+eWMnxuKedIn53dbteVK1eUN29eeXgk/k0xDllMRR4eHvetXyv4+/tb/ofRGfG5pRyfXcrwuaUMn1vK8dmlDJ9byvC5pQyfW8o5ymcXEBDwwG1Y1AMAAAAALEKQAQAAAIBFCDIX5e3trZEjR8rb29vqUZwKn1vK8dmlDJ9byvC5pRyfXcrwuaUMn1vK8LmlnDN+dizqAQAAAAAWYQ8ZAAAAAFiEIAMAAAAAixBkAAAAAGARggwAAAAALEKQOYhRo0apZMmSypQpk7JmzaratWtr165d8bb59ddf1aRJE2XPnl3+/v6qVq2aNm7cmODz/f3338qXL59sNpsuX74ce394eLjatWun4sWLy8PDQ/3790/yjHPmzFG5cuXk4+OjnDlzqm/fvil5q6kuKZ9d48aNVaBAAfn4+ChPnjzq2LGjzpw5E2+bn376Sc8995x8fHyUP39+vf/++/F+HxQUJJvNds+lQYMGic62bds2Va1aVYGBgfL19VXJkiU1adKk1HvzKXT79m29+eabevzxx5UpUyblzZtXnTp1iveZbNq0KcH3a7PZtGfPHknms0/o95kyZYp9nqVLl+rJJ59UlixZlClTJj3xxBP64osvHjjjpk2bVLFiRXl7e6tYsWKaM2dOqn8OyZWUz+3EiRPq1q2bChcuLF9fXxUtWlQjR47UrVu3YrcJCwtTjRo1lCtXLvn4+KhIkSIaNmyYbt++He/1PvzwQ5UoUUK+vr7Knz+/BgwYoJs3byY6X1L+97DK0qVLFRwcrMDAQNlsNh04cCDe70+cOJHon7fFixfHbnfy5Ek1aNBAjzzyiHLmzKnXX39dd+7cSfA1v//+e3l5eemJJ55I8pxHjx6Vn5+fsmTJkoJ3mfoe9LlJ0owZMxQUFCR/f/97/p0vJe2fZenB/w5MSL9+/VSpUiV5e3sn63O2yuHDh9W4cWMFBAQoU6ZMeuqpp3Ty5MnY3/fq1UtFixaVr6+vcuTIoSZNmujIkSPxniOhz3HBggX3fd3k/P3taJLyZ/DmzZvq27evAgMDlTlzZrVo0ULnzp2Lt01K/qwk9Pdu7969U+FdpZ+xY8fKZrPF+/9bD/pz9uOPP6pt27bKnz+/fH19VapUKU2ePPmBr+XMf85iXLlyRf3791fBggXl6+urKlWqxPv3lHT/f46T+nfJvyXlz3laIsgcRPHixTV16lQdPHhQ27ZtU6FChRQcHKy//vordpuGDRvqzp072rBhg/bt26fy5curYcOGOnv27D3P161bN5UrV+6e+6OiopQjRw4NGzZM5cuXT/J8EydO1NChQzV48GD9/PPPWrdunerWrZuyN5vKkvLZ1ahRQ4sWLVJYWJiWLFmiY8eOqWXLlrG/j4yMVHBwsAoWLKh9+/Zp/PjxGjVqlGbMmBG7zdKlSxUeHh57OXTokDw9PdWqVatEZ8uUKZNefvllbdmyRYcPH9awYcM0bNiweM9rhevXr2v//v0aPny49u/fr6VLlyosLEyNGzeO3aZKlSrx3m94eLi6d++uwoUL68knn5QkDRo06J5tSpcuHe8zyZYtm4YOHaodO3bop59+UpcuXdSlSxetXbs20fmOHz+uBg0aqEaNGjpw4ID69++v7t273/cx6SEpn9uRI0cUHR2t6dOn6+eff9akSZP0ySef6D//+U/sNhkyZFCnTp303XffKSwsTB9++KE+/fRTjRw5MnabefPmafDgwRo5cqQOHz6smTNnauHChfGe59+S8r+HVa5du6Zq1app3LhxCf4+f/7898w+evRoZc6cWfXr15ck3b17Vw0aNNCtW7e0fft2hYSEaM6cORoxYsQ9z3f58mV16tRJtWrVSvKMt2/fVtu2bfXcc8+l7E2mgQd9bpL5c1mvXr1E/2wk5Z/lpPw7MDFdu3bViy++mLI3mI6OHTumatWqqWTJktq0aZN++uknDR8+XD4+PrHbVKpUSbNnz9bhw4e1du1a2e12BQcH6+7du/Gea/bs2fE+z6ZNm973tZPz97ejScqfwQEDBmjlypVavHixNm/erDNnzqh58+b3bJeSPys9evSI91kn5T8UOIo9e/Zo+vTp9/z/sQf9Odu3b59y5sypuXPn6ueff9bQoUM1ZMgQTZ069b6v58x/zmJ0795doaGh+uKLL3Tw4EEFBwerdu3aOn36tKQH/3OclL9LEpKUP+dpyg6HFBERYZdkX7dund1ut9v/+usvuyT7li1bYreJjIy0S7KHhobGe+zHH39sf/755+3r16+3S7JfunQpwdd4/vnn7a+++uoDZ7l48aLd19c3dhZH9+/PLiHLly+322w2+61bt+x2u/nMsmbNao+Kiord5s0337SXKFEi0eeYNGmS3c/Pz3716tVkzdesWTN7hw4dkvWY9LB79267JPsff/yR4O9v3bplz5Ejh/2tt95K9DkOHDhwz5/ThFSoUME+bNiwRH//xhtv2MuUKRPvvhdffNFet27d+z6vFR70udntdvv7779vL1y48H2fZ8CAAfZq1arF3u7bt6+9Zs2a8bYZOHCgvWrVqkmeLan/e/x/e/ceFWWZxwH8NzcuQ0AgYHJREwSvgZrWqIurphwsLbZNS7RSz7qmdU6dvCxp0VJy2ePprJG2akrrFY+tmXkhDbEDOlnoIAgGCCqX3HW9gCHEbb77hzvv4WXemXkHkAH7fc6ZP3je533e933muby/9zJ0p8uXL4OIYDAYbOaNiIjAwoULhb+PHDkCpVKJf//730Lap59+Cg8PD1HfBe61lzVr1iA+Ph7h4eGy9m3lypWYN28e0tLS4OnpKWud7iKn3rKysqyO+SZSfbkjY2Bb9tSzo8yZM8fusff8+fMgIly6dElIIyJ8+eWXssuwZ/7uySy1wZqaGmg0Guzbt09Iu3jxIogIer3erBx72orc85Se6JdffsHgwYNx/Phxm8ch1c7aW7p0KSZPnmxx+YPQzurr66FSqXDo0CFR+ujRo7F69WoAHevH7ecSa+yZo7oS3yHrgZqammjz5s3k6ekp3MXq06cPhYWF0fbt2+nu3bvU0tJCmzZtIj8/PxozZoywblFRESUkJND27dtJqeyar/f48eNkNBqpurqahg4dSoGBgTR79myqrKzskvK7klTdtXfr1i3atWsXjR8/njQaDRER6fV6ioyMJCcnJyFfVFQUFRcX0+3btyXL2bp1K7344ot2PQ5mMBjo9OnTNGnSJDuOqnvU1taSQqGw+KjWwYMH6ebNm7RgwQKLZXz22WcUGhpq8Q4DAMrMzKTi4mKKjIy0WI5er6ennnpKlBYVFUV6vd72gXQzW/VmyuPt7W1x+aVLlygjI0PULsaPH09nz56lH374gYiIysvL6ciRIzRjxgzZ+2br++jJzp49S3l5ebRo0SIhTa/X08iRI6lv375CWlRUFN25c4cKCwuFtLS0NCovLxfdcbTlxIkTtG/fPtqwYUPXHEAPJtWXOzIG9iZGo5EOHz5MoaGhFBUVRX5+fvTEE0/QgQMHLK5z9+5dSktLo0cffZSCgoJEy5YtW0Y+Pj40btw42rZtG8HKv3SVO3/3VmfPnqXm5mbRmD1kyBDq379/l4zZu3btIh8fHxoxYgTFxcVRfX19p8vsDsuWLaOnn37abC5rz1o7a8vWPPIgtLOWlhZqbW0V3bUmInJ1daWcnJwO9WOpuaRH6tbwj1n19ddfw83NDQqFAv7+/vjhhx9EyysrKzFmzBgoFAqoVCr069cP586dE5b/+uuveOyxx7Bjxw4Atq+Wyr3ylJSUBI1Gg7CwMGRkZECv12Pq1KkICwszuyrtKLbqDrh39Vur1YKI8OSTT+LGjRvCsmnTpmHx4sWi/IWFhSAiFBUVmZV15swZEBHOnDkja/8CAgLg5OQEpVJp9Q6TozQ0NGD06NGYO3euxTzR0dGIjo62WoaXlxdSUlLMltXU1MDNzQ1qtRrOzs7YunWr1f0ZPHgwEhMTRWmHDx8GEaG+vt7G0XQfOfVWWloKDw8PbN682WyZTqeDs7MziAiLFy9Ga2uraPn69euh0WigVqtBRFiyZIld+2bp+3AkuVcfX3vtNQwdOlSU9qc//QnTp08Xpd29exdEhCNHjgAASkpK4Ofnh+LiYgDyrsbfuHEDQUFB+O677wDggb9DJtWX7R0D2+vpd8iuXbsGIoJWq8VHH30Eg8GApKQkKBQKnDx5UpR3w4YNcHNzAxEhLCzM7K5FQkICcnJycO7cOSQnJ8PZ2Rnr16+3un1b83dvYKkN7tq1C05OTmb5x44di5UrV5ql29NWNm3ahIyMDOTn52Pnzp0ICAhATExMR3a/W+3ZswcjRoxAQ0MDAOnzLVvtrK1Tp05BrVbjm2++sbrdB6Gd6XQ6TJo0CdXV1WhpacGOHTugVCoRGhpqVz82kZpLrHHUHTIOyBxg586dcHNzEz6m28t1dXUoLS2FXq/HwoULMXDgQPznP/8BABiNRsyaNQvR0dHIycnB2bNn8dprryEgIAA///wzgHuPPM2ZM0fYTlcFZGvXrgURiQaC69evQ6lUIiMjo4O10DEdqTuT//73vyguLsaxY8cwYcIEzJgxA0ajEYD9JyOLFy/GyJEjZe93eXk58vPzsXnzZnh7e2P37t32HnqnWKo34N7jSzNnzsSoUaNQW1sruX5lZSWUSiW++OILi9vYvXs31Gq16HEyk9bWVpSWlsJgMGDdunXw9PREVlaWxbJ6SkDW2XqrqqpCcHAwFi1aJLm8oqIChYWF2L17NwICAkTBU1ZWFvr27YstW7YgPz8f+/fvR1BQkOyA3tr3cb9Zqzc5k119fT08PT2xbt06UbqtgKylpQWPP/44Pv30U2G5nJO/mJgYrFq1SvjbUQFZZ+tNTkBmqS8/aAFZ+7o8efIkiAgvvfSSKN/MmTPx4osvitJqampQUlKC7777DjNnzsTo0aOFE2sp7777LgIDAy0ulzN/9xQdaYP3MyBrz/QqhrXgxdEqKirg5+eH8+fPC2lS51ty21lBQQF8fHzwwQcfWN1ub2pn1ly6dAmRkZEgIqhUKowdOxaxsbEYMmQIqqurZfdjwPJcYg0HZL8hd+7cQWlpqfCxdIIZEhIinJR+++23UCqVZid+ISEhSEpKAgCEh4dDqVRCpVJBpVJBqVQKDfq9994zK19uQLZt2zYQESorK0Xpfn5+klf976eO1J2UyspKEBFOnz4NAJg/fz6effZZUZ4TJ06AiHDr1i1Rel1dHTw8PPD3v/+9Q8fwwQcfIDQ0tEPrdpSlemtqasJzzz2Hxx57THTHsL2EhAT4+voK79xJmTJlCp577jlZ+7No0SKzE+u2fve735m1zW3btsHDw0NW+V2lM/VWXV2NwYMHY/78+WZ3vqTs2LEDrq6uaGlpAQBMnDgRy5cvl8wjpzx7vo+uZq2fypnstm/fDo1Gg+vXr4vS3333XbMTufLychARzp07h9u3bwtjnumjUCiEtMzMTMnteXp6itZpO3baupvblTpbb3ICMkt92Z4xUEpPC8ja12VNTQ3UarXZSe3KlSsxfvx4i+U0NjZCq9VavYh26NAhEBF+/fVXyeVy5u+eoiNt0NL76v3798dHH31kto3OtJW6ujoQUbdfDLbHl19+aTYOEZFw18o0xrdlqZ0VFhbCz88P77zzjs3t9qZ2JkddXZ0QSM6ePRszZsxAY2OjXf3Y0lxijaMCMnXXPgDJ5HB3dyd3d3eb+YxGIzU2NhIRCc9Mt38vTKlUktFoJCKif/3rX9TQ0CAs+/HHH2nhwoWUnZ1NwcHBHd7fCRMmENG9n+oODAwkonvvYd24cYMGDBjQ4XI7oiN1Z2k5EQl5dDodrV69mpqbm4X3yo4fP05hYWHk5eUlWnffvn3U2NhI8+bN69Ax2Nq3+0Gq3pqbm2n27NlUWlpKWVlZ1KdPH8l1AVBaWhq9/PLLQt20d/nyZcrKyqKDBw/K2h9bdaDT6ejIkSOitOPHj5NOp5NVflfpaL1VV1fT5MmThV/SkvM+p9FopObmZjIajaRSqai+vt5sPZVKRURk9X0VIvu/j64mt59asnXrVpo1axb5+vqK0nU6Ha1du5auX79Ofn5+RHSvXXh4eNCwYcNIo9FQQUGBaJ2NGzfSiRMn6IsvvqBHH31Ucnt6vV70K3pfffUVpaSk0OnTpykgIKDDx2GvztabLdb6sj1jYG8gVZdjx46l4uJiUVpJSYnVeQz3LlxbHa/y8vLIy8uLnJ2dJZfLmb97io60wTFjxpBGo6HMzEx6/vnnieje+UJFRUWXj9mmnyLv169fl5bblaZOnWo2Di1YsICGDBlCq1atEsbxtqTaWWFhIU2ZMoVeeeUVWrt2rc3t9qZ2Joebmxu5ubnR7du36ZtvvqG//e1v5OTkZFc/tjSX9EjdGv4xSXV1dYiLi4Ner8eVK1eQm5uLBQsWwNnZGRcuXABw73G7Pn364A9/+APy8vJQXFyM5cuXQ6PRIC8vT7JcS1dLDQYDDAYDxowZg7lz58JgMKCwsFBYvn//frNf1nr22WcxfPhwnDp1CgUFBXjmmWcwbNgwq3dMuoOcuvv++++RmpoKg8GAK1euIDMzE+PHj0dwcLBwRbOmpgZ9+/bF/PnzceHCBaSnp0Or1WLTpk1m25w4caLo0dC2/vKXv2D+/PnC35988gkOHjyIkpISlJSU4LPPPoO7u7vwa0GO0tTUhFmzZiEwMBB5eXm4du2a8Gn/XuC3334LIsLFixctlrdmzRr4+/tLXvlLTEzEsWPHUFZWhqKiIqxbtw5qtRpbtmwR8rSvt/Lycmi1WqxYsQIXL17Ehg0boFKpHH5VVE69VVVVISQkBFOnTkVVVZUoj8nOnTuxd+9eFBUVoaysDHv37oW/vz9iY2OFPPHx8XB3d8eePXtQXl6OY8eOITg4GLNnzxbypKammv0SI2D9+3CUmzdvwmAwCI+epqenw2AwiOoFuPfOnUKhwNGjR83KaGlpwYgRIzB9+nTk5eUhIyMDvr6+iIuLs7hdqavxlurNpCe9Qyan3q5duwaDwYAtW7YIv7JmMBhw8+ZNUVnW+rKcMVBqbjA9ivznP/8ZoaGhwvzSU94vbmv//v3QaDTYvHkzSktLkZqaCpVKhezsbABAWVkZEhMTkZubi6tXr+LUqVOYOXMmvL29hUfgDx48iC1btqCgoAClpaXYuHEjtFqt6CmUM2fOICwsDFVVVQA6Nn/3JHLa4JIlS9C/f3+cOHECubm50Ol00Ol0onJstZWqqiqEhYUJ72ZfunQJCQkJyM3NxeXLl/HVV19h0KBBiIyM7L6D7yJtn0iS084KCgrg6+uLefPmieaQtnd6HrR2ZpKRkYGjR48K8154eDieeOIJ4XzTVj82sTaXAEBYWBj2798v/C13jrpfOCDrARoaGhATEwN/f384OTmhX79+mDVrltkPU/z444+YPn06vL294e7ujieffFJ4kV2KpYCMiMw+AwYMEJanpaWhfaxeW1uLhQsX4uGHH4a3tzdiYmJQUVHR6WPvLDl1l5+fj8mTJ8Pb2xvOzs4YOHAglixZIgxiJufPn8fEiRPh7OyMgIAAJCcnm23vp59+AhHh2LFjkvvzyiuvYNKkScLfH3/8MYYPHw6tVgsPDw+MGjUKGzdulPXI2f1kuiUv9Wn/btdLL71k9ZGe1tZWBAYGWnykYvXq1QgJCYGLiwu8vLyg0+mQnp4uytO+3oB77TciIgJOTk4YNGgQ0tLSOnKoXUpOvZn6j9THJD09HaNHj8ZDDz0ENzc3DBs2DImJiaL3B5qbm/H+++8jODgYLi4uCAoKwtKlS0X9OT4+XtR3Advfh6NYqpf4+HhRvri4OAQFBVnsI1euXEF0dDRcXV3h4+ODt99+G83NzRa3KxWQSdVb+33tKQGZnHqLj4+XzNO+z9jqy7bGQKm5YdKkSZLbvnz5cmcP/b7YunWrMB6Fh4fjwIEDwrLq6mpER0fDz88PGo0GgYGBmDt3Ln766Schz9GjRxERESH03fDwcPzjH/8QtVfT3Nu2Duydv3sSOW2woaEBS5cuhZeXF7RaLWJiYsxOZG21FdP4ahpLKyoqEBkZKczdISEhWLFihcV3dnuytgGZnHZmqU+3HbcetHZmsnfvXgwaNAhOTk545JFHsGzZMtTU1IjyWOvHJrbmkvZjpNw56n5R/H+nGGOMMcYYY4x1M/4/ZIwxxhhjjDHmIByQMcYYY4wxxpiDcEDGGGOMMcYYYw7CARljjDHGGGOMOQgHZIwxxhhjjDHmIByQMcYYY4wxxpiDcEDGGGOMMcYYYw7CARljjDHGGGOMOQgHZIwxxlgnZGZm0tChQ6m1tZWIiN5//32KiIjodLlNTU00cOBAys3N7XRZjDHGei4OyBhjjP2mvfrqq6RQKCg5OVmUfuDAAVIoFDbXX7lyJa1Zs4ZUKpWs7Z08eZIUCgUNHz5cCOJMHn74Yfr888+JiMjJyYmWL19Oq1atkncgjDHGeiUOyBhjjP3mubi4UEpKCt2+fduu9XJycqisrIyef/55u7dZXl5O27dvt5onNjaWcnJyqLCw0O7yGWOM9Q4ckDHGGPvNe+qpp+iRRx6hpKQku9ZLT0+nadOmkYuLi8U8ZWVlNGjQIHr99dcJgJD+xhtvUHx8PDU2Nlpc18vLiyZMmEDp6el27RdjjLHegwMyxhhjv3kqlYoSExMpNTWVqqqqZK+XnZ1Njz/+uMXl+fn5NHHiRJo7dy598sknokcg33zzTWppaaHU1FSr2xg3bhxlZ2fL3ifGGGO9CwdkjDHGGBHFxMRQREQExcfHy17n6tWr5O/vL7ns9OnT9Pvf/56WL19OH374odlyrVZL8fHxlJSURLW1tRa34e/vT1evXpW9T4wxxnoXDsgYY4yx/0tJSaF//vOfdPHiRVn5GxoaJB9XrKiooGnTptF7771Hb7/9tsX1Fy1aRH369KGUlBSLeVxdXam+vl7W/jDGGOt9OCBjjDHG/i8yMpKioqIoLi5OVn4fHx/JHwLx9fWlcePG0Z49e+jOnTsW11er1bR27Vpav349/fzzz5J5bt26Rb6+vvIOgDHGWK/DARljjDHWRnJyMn399dek1+tt5h01ahQVFRWZpbu6utKhQ4fIxcWFoqKi6JdffrFYxgsvvEDDhw+nv/71r5LLL1y4QKNGjZJ/AIwxxnoVDsgYY4yxNkaOHEmxsbH08ccf28wbFRVFOTk5ksvc3Nzo8OHDpFarKTo6murq6iyWk5ycTNu2baO7d++aLcvOzqbp06fLPwDGGGO9CgdkjDHGWDsJCQlkNBpt5ouNjaXCwkIqLi6WXP7QQw/R0aNHCQA9/fTTkgEXEdGUKVNoypQp1NLSIkrX6/VUW1tLf/zjH+0/CMYYY72CAm3/KQpjjDHG7LJixQq6c+cObdq0qcvLnjNnDoWHh9M777zT5WUzxhjrGfgOGWOMMdYJq1evpgEDBsi6o2aPpqYmGjlyJL311ltdWi5jjLGehe+QMcYYY4wxxpiD8B0yxhhjjDHGGHMQDsgYY4wxxhhjzEE4IGOMMcYYY4wxB+GAjDHGGGOMMcYchAMyxhhjjDHGGHMQDsgYY4wxxhhjzEE4IGOMMcYYY4wxB+GAjDHGGGOMMcYchAMyxhhjjDHGGHOQ/wHl1V7PYEQfgwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "concrete = ConcreteEC2_2004(25)\n", + "reinforcement = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07) \n", + "# Create section\n", + "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n", + "geo = SurfaceGeometry(poly, concrete)\n", + "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcement, n=3)\n", + "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n", + "sec = GenericSection(geo)\n", + "# sec.geometry = sec.geometry.translate(0, -sec.gross_properties.cz) # -> uncomment this line and results will improve\n", + "draw_section(sec)\n", + "\n", + "\n", + "theta = 0\n", + "res = sec.section_calculator.calculate_nm_interaction_domain(theta)\n", + "draw_N_M_diagram(res)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "11. Moment curvature results (fib package vs FH-PIEM)\n", + "- Something fails with axial load. if center of gravity is in the origin it works ok.\n", + "- Why only return the negative domain of curvature with theta=0? Wouldn't it be better to have the whole domain instead of use theta parameter for that?\n", + "- The moment-curvature results should start at (0,0). I miss that point." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "concrete = ConcreteEC2_2004(25)\n", + "reinforcement = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07,) \n", + "# Create section\n", + "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n", + "geo = SurfaceGeometry(poly, concrete)\n", + "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcement, n=3)\n", + "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n", + "sec = GenericSection(geo)\n", + "#sec.geometry = sec.geometry.translate(0, -sec.gross_properties.cz) # -> uncomment this line and results will improve\n", + "\n", + "n=-1500 * 1e3\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.set_title(f'M-X N={n/1e3} kN')\n", + "ax.set_xlabel('X (1/km)')\n", + "ax.set_ylabel('My (mkN)')\n", + "ax.axhline(0, color='gray', linewidth=1)\n", + "ax.axvline(0, color='gray', linewidth=1)\n", + "xmin,xmax,ymin,ymax = 0,0,0,0\n", + "\n", + "res = sec.section_calculator.calculate_moment_curvature(theta=0, n=n)\n", + "ax.plot(res.chi_y*1e6, res.m_y/1e6, color='red', label='N=-1500 fib')\n", + "xmin = min(xmin, np.min(res.chi_y*1e6))\n", + "ymin= min(ymin,np.min(res.m_y/1e6))\n", + "xmax = max(xmax, np.max(res.chi_y*1e6))\n", + "ymax= max(ymax, np.max(res.m_y/1e6))\n", + "res = sec.section_calculator.calculate_moment_curvature(theta=math.pi, n=n)\n", + "ax.plot(res.chi_y*1e6, res.m_y/1e6, color='red')\n", + "xmin = min(xmin, np.min(res.chi_y*1e6))\n", + "ymin= min(ymin,np.min(res.m_y/1e6))\n", + "xmax = max(xmax, np.max(res.chi_y*1e6))\n", + "ymax= max(ymax, np.max(res.m_y/1e6))\n", + "\n", + "#FH results\n", + "m1=[-324.42,-324.36,-324.3,-324.24,-324.18,-324.11,-324.03,-323.96,-323.87,-323.79,-323.69,-323.44,-322.81,-322.15,-321.47,-320.77,-320.03,-319.27,-318.47,-317.64,-316.78,-315.88,-314.93,-313.95,-312.91,-311.83,-310.69,-309.49,-308.23,-306.9,-305.35,-301.59,-297.76,-293.86,-289.88,-285.81,-281.64,-277.34,-272.91,-268.33,-263.57,-258.61,-253.43,-247.98,-242.22,-236.1,-229.57,-222.51,-214.83,-206.39,-196.94,-186.25,-173.91,-159.25,-141.39,-120.32,-99.1,-78.07,-56.94,-36.38,-15.46,-5.13,4.79,14.53,24.24,34.05,43.77,53.33,62.79,72.2,81.47,90.62,99.68,108.67,117.47,126.07,134.19,141.85,149.03,155.84,162.33,168.53,174.44,180.1,185.56,190.79,195.81,200.63,205.29,209.77,214.05,218.17,222.14,225.95,229.61,233.15,236.56,239.86,243.06,246.18,249.21,252.16,255.04,257.28,259.23,261.12,262.94,264.7,266.41,268.06,269.66,271.21,272.72,274.18,275.6,276.98,278.33,279.64,280.91,282.15,283.37\n", + "]\n", + "x1=[-19.92,-19.58,-19.25,-18.92,-18.59,-18.26,-17.92,-17.59,-17.26,-16.93,-16.6,-16.26,-15.93,-15.6,-15.27,-14.94,-14.6,-14.27,-13.94,-13.61,-13.28,-12.94,-12.61,-12.28,-11.95,-11.62,-11.29,-10.95,-10.62,-10.29,-9.96,-9.63,-9.29,-8.96,-8.63,-8.3,-7.97,-7.63,-7.3,-6.97,-6.64,-6.31,-5.97,-5.64,-5.31,-4.98,-4.65,-4.31,-3.98,-3.65,-3.32,-2.99,-2.66,-2.32,-1.99,-1.66,-1.33,-1,-0.66,-0.33,0,0.16,0.33,0.49,0.65,0.81,0.98,1.14,1.3,1.46,1.63,1.79,1.95,2.11,2.28,2.44,2.6,2.76,2.93,3.09,3.25,3.41,3.58,3.74,3.9,4.06,4.23,4.39,4.55,4.71,4.88,5.04,5.2,5.37,5.53,5.69,5.85,6.02,6.18,6.34,6.5,6.67,6.83,6.99,7.15,7.32,7.48,7.64,7.8,7.97,8.13,8.29,8.45,8.62,8.78,8.94,9.1,9.27,9.43,9.59,9.75\n", + "]\n", + "ax.plot(x1, m1, color='blue', label='N= 1500 FH', linestyle='--')\n", + "\n", + "n=0\n", + "res = sec.section_calculator.calculate_moment_curvature(theta=0, n=n)\n", + "ax.plot(res.chi_y*1e6, res.m_y/1e6, color='green', label='N= 0 fib')\n", + "xmin = min(xmin, np.min(res.chi_y*1e6))\n", + "ymin= min(ymin,np.min(res.m_y/1e6))\n", + "xmax = max(xmax, np.max(res.chi_y*1e6))\n", + "ymax= max(ymax, np.max(res.m_y/1e6))\n", + "res = sec.section_calculator.calculate_moment_curvature(theta=math.pi, n=n)\n", + "ax.plot(res.chi_y*1e6, res.m_y/1e6, color='green')\n", + "xmin = min(xmin, np.min(res.chi_y*1e6))\n", + "ymin= min(ymin,np.min(res.m_y/1e6))\n", + "xmax = max(xmax, np.max(res.chi_y*1e6))\n", + "ymax= max(ymax, np.max(res.m_y/1e6))\n", + "\n", + "#FH results\n", + "m1=[-65.77,-65.76,-65.74,-65.73,-65.71,-65.69,-65.67,-65.65,-65.63,-65.61,-65.58,-65.56,-65.53,-65.5,-65.47,-65.44,-65.41,-65.38,-65.34,-65.3,-65.26,-65.21,-65.17,-65.12,-65.06,-65.01,-64.94,-64.88,-64.81,-64.73,-64.65,-64.57,-64.48,-64.39,-64.3,-64.2,-64.09,-63.98,-63.87,-63.76,-63.64,-63.52,-63.39,-63.26,-63.12,-62.99,-62.84,-62.7,-62.55,-62.39,-62.23,-62.06,-61.88,-61.7,-61.49,-61.26,-51.88,-38.96,-26,-13,0,16.12,32.23,48.29,64.12,79.85,95.42,110.84,126.11,141.19,156.11,170.82,185.35,199.67,213.78,227.66,241.3,254.69,267.82,280.66,293.21,305.43,310.76,311.55,312.26,312.92,313.52,314.07,314.58,315.05,315.48,315.88,316.26,316.61,316.94,317.25,317.55,317.82,318.09,318.34,318.58,318.81,319.03,319.24,319.44,319.64,319.83,320.01,320.19,320.36,320.52,320.68,320.84,320.99,321.14,321.28,321.42,321.51,321.55,321.59,321.62\n", + "]\n", + "x1=[-73.87,-72.64,-71.4,-70.17,-68.94,-67.71,-66.48,-65.25,-64.02,-62.79,-61.56,-60.32,-59.09,-57.86,-56.63,-55.4,-54.17,-52.94,-51.71,-50.48,-49.24,-48.01,-46.78,-45.55,-44.32,-43.09,-41.86,-40.63,-39.4,-38.16,-36.93,-35.7,-34.47,-33.24,-32.01,-30.78,-29.55,-28.32,-27.08,-25.85,-24.62,-23.39,-22.16,-20.93,-19.7,-18.47,-17.24,-16,-14.77,-13.54,-12.31,-11.08,-9.85,-8.62,-7.39,-6.16,-4.92,-3.69,-2.46,-1.23,0,0.41,0.82,1.23,1.63,2.04,2.45,2.86,3.27,3.68,4.09,4.5,4.9,5.31,5.72,6.13,6.54,6.95,7.36,7.76,8.17,8.58,8.99,9.4,9.81,10.22,10.63,11.03,11.44,11.85,12.26,12.67,13.08,13.49,13.9,14.3,14.71,15.12,15.53,15.94,16.35,16.76,17.16,17.57,17.98,18.39,18.8,19.21,19.62,20.03,20.43,20.84,21.25,21.66,22.07,22.48,22.89,23.29,23.7,24.11,24.52\n", + "]\n", + "ax.plot(x1, m1, color='orange', label='N= 0 FH', linestyle='--')\n", + "\n", + "x_legend = np.linspace(xmin, xmax, 10)\n", + "y_legend = np.linspace(ymin, ymax, 10)\n", + "ax.set_xticks(x_legend)\n", + "ax.set_yticks(y_legend)\n", + "ax.set_xticklabels(np.around(x_legend, decimals=1))\n", + "ax.set_yticklabels(np.around(y_legend, decimals=1))\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12. When calculating the geometric properties EA, EI uses the tangent modulus at eps=0. With concrete it does not use Ecm. \n", + "In order to homogenise a section it is now necessary to adapt Es =Ec,tang.\n", + "Would be usefull to get section properties in homogenised section using Ecm. Instead of using E=constitutive_law.get_tangent(0)[0], a parameter of E could be entered for each material just for gross properties calculation. The default should be Ecm for concrete and constitutive_law.get_tangent(0)[0] for others.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Should be SurfaceGeometry class inherited from Geometry class??" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Moment-Curvature.png b/Moment-Curvature.png new file mode 100644 index 00000000..d0e88f3a Binary files /dev/null and b/Moment-Curvature.png differ diff --git a/My_Mz_N_-500kN.png b/My_Mz_N_-500kN.png new file mode 100644 index 00000000..c5671e4e Binary files /dev/null and b/My_Mz_N_-500kN.png differ diff --git a/My_Mz_N_0kN.png b/My_Mz_N_0kN.png new file mode 100644 index 00000000..67685525 Binary files /dev/null and b/My_Mz_N_0kN.png differ diff --git a/N2000_my_min.png b/N2000_my_min.png new file mode 100644 index 00000000..a012f3e4 Binary files /dev/null and b/N2000_my_min.png differ diff --git a/N_My.png b/N_My.png new file mode 100644 index 00000000..942a04c6 Binary files /dev/null and b/N_My.png differ diff --git a/SLS_section_response.py b/SLS_section_response.py new file mode 100644 index 00000000..e92858cf --- /dev/null +++ b/SLS_section_response.py @@ -0,0 +1,309 @@ +import math + +import numpy as np +from shapely.geometry import LineString, Polygon + +from structuralcodes.codes import _CODE, get_design_codes, set_design_code +from structuralcodes.geometry import CompoundGeometry, SurfaceGeometry +from structuralcodes.materials.concrete import create_concrete +from structuralcodes.materials.constitutive_laws import ( + ParabolaRectangle, + Sargin, + UserDefined, +) +from structuralcodes.sections._generic import GenericSection + + +def calculate_strain_profile_batch(sec: GenericSection, n_ed, my_ed): + """'Get the moment curvature diagram. Then interpolate to get curvature for my_d. + + Args: + n_ed [N]: Axial load + my_ed [N*m]: List of bending moment My + + Returns: + eps_a: strain at (0,0) + chi: curvature of the section + """ + my_ed = np.array(my_ed, dtype=float) + result_neg = sec.section_calculator.calculate_moment_curvature(0, n=n_ed) + result_pos = sec.section_calculator.calculate_moment_curvature( + math.pi, n=n_ed + ) + chi_y = np.vstack((result_neg.chi_y, result_pos.chi_y)).reshape(-1) + # results.m_y in (Nmm) + m_y = (np.vstack((result_neg.m_y, result_pos.m_y))).reshape(-1) + eps_axial = np.vstack( + (result_neg.eps_axial, result_pos.eps_axial) + ).reshape(-1) + # sorts results M-chi + index = np.argsort(chi_y) + chi_y = chi_y[index] + m_y = m_y[index] + eps_axial = eps_axial[index] + + chi_y_ = np.interp(my_ed, m_y, chi_y) # results.m_y in (Nmm) + epsa_ = np.interp(my_ed, m_y, eps_axial) + return epsa_, chi_y_ + + +def calculate_strain_profile(section: GenericSection, n_ed=0, m_ed=0): + """'Get the stress in a given point (y,z) inside the cross section. + + Args: + n_ed [N]: Axial load + m_ed [N*m]: Bending moment My + + Returns: + eps_a: strain at (0,0) + chi: curvature of the section + """ + # Rotate the section of angle theta + sec_geom = section.geometry + + def interpolate(x1, x2, y1, y2, x): + if x2 - x1 == 0: + return y1 + y = y1 + (y2 - y1) * (x - x1) / (x2 - x1) + return y + + # Check if the section can carry the axial load + section.section_calculator.check_axial_load(n=n_ed) + + if m_ed < 0: + r = section.section_calculator.calculate_bending_strength( + theta=0, n=n_ed + ) + m1, m2 = r.m_y, 0 + chi1, chi2 = r.chi_y, 0 + else: + r = section.section_calculator.calculate_bending_strength( + theta=math.pi, n=n_ed + ) + m1, m2 = 0, r.m_y + chi1, chi2 = 0, -r.chi_y + + chi = interpolate(m1, m2, chi1, chi2, m_ed) + + # Previous position of strain at (0,0) + strain = [0, 0, 0] + + ITMAX = 200 + tolerance = 1000 # (1e-3 mkN) + iter = 0 + My = 1e11 + + while abs(m_ed - My) > tolerance and iter < ITMAX: + strain = section.section_calculator.find_equilibrium_fixed_curvature( + sec_geom, n_ed, chi, strain[0] + ) + ( + _, + My, + Mz, + _, + ) = section.section_calculator.integrator.integrate_strain_response_on_geometry( + geo=sec_geom, + strain=strain, + tri=section.section_calculator.triangulated_data, + ) + + if My > m_ed: + m2 = My + chi2 = chi + else: + m1 = My + chi1 = chi + + chi = interpolate(m1, m2, chi1, chi2, m_ed) + eps_a = strain[0] + iter += 1 + + if iter == ITMAX: + return None, None + else: + return eps_a, chi + + +def calculate_cracking_moment(section: GenericSection, n=0, plot=False): + """Calculate the cracking moment of a R.C section in N*mm. + A concrete material failing at fctm is used for the cracking moment + calculation. This method modify the constitutive law of concrete to reach + fctm in tension. + + Args: + n [N]: Axial external load + + Returns: + m_cracking_pos: positive My_cracking + m_cracking_neg: negative My_cracking + """ + + def modify_consitutive_law(geom: SurfaceGeometry): + mat = geom.material + gamma_c = 1.5 # !!!! Temporary. gamma_c should be readable from ConstitutiveLaw class (TODO) + if ( + _CODE is None + ): # should be the code of the material used to create the section + set_design_code(get_design_codes()[1]) + if isinstance(mat, ParabolaRectangle): + strains = np.linspace(mat._eps_u, 0, 20) + aux_concrete = create_concrete(abs(mat._fc * gamma_c), gamma_c=1) + elif isinstance(mat, Sargin): + strains = np.linspace(mat._eps_cu1, 0, 20) + aux_concrete = create_concrete(abs(mat._fc * gamma_c), gamma_c=1) + else: # UserDefined + strains = np.linspace(-0.0035, 0, 20) + eps_max, eps_min = mat.get_ultimate_strain() + s1 = np.linspace(eps_min, eps_max, 100) + s2 = mat.get_stress(s1) + aux_concrete = create_concrete(abs(s2.min() * gamma_c), gamma_c=1) + + stress = aux_concrete.constitutive_law.get_stress((strains)) + # stress = mat.get_stress((strains)) + strains = strains.tolist() + stress = stress.tolist() + + fctm = aux_concrete.fctm + Ec = aux_concrete.constitutive_law.get_tangent(0)[0] + tensile_strain_limit = fctm / Ec + strains.append(tensile_strain_limit) + stress.append(fctm) + ec_const = UserDefined( + strains, stress, 'constitutive_law_with_tension' + ) + geom.material = ec_const + if plot: + from structuralcodes.plots.section_plots import ( + draw_constitutive_law, + ) + + draw_constitutive_law(ec_const) + + import copy + + sec = copy.deepcopy(section) + m_cracking_pos, m_cracking_neg = 0, 0 + if sec.geometry.reinforced_concrete: + if isinstance(sec.geometry, SurfaceGeometry): + modify_consitutive_law(sec.geometry) + elif isinstance(sec.geometry, CompoundGeometry): + for i in range(len(sec.geometry.geometries)): + modify_consitutive_law(sec.geometry.geometries[i]) + else: + raise ValueError( + 'geometry must be SurfaceGeometry or CompoundGeometry' + ) + + m_cracking_pos = sec.section_calculator.calculate_bending_strength( + theta=math.pi, n=n + ).m_y + m_cracking_neg = sec.section_calculator.calculate_bending_strength( + theta=0, n=n + ).m_y + + # PLOT STRESS DIAGRAM + if plot: + from structuralcodes.plots.section_plots import ( + draw_section_response, + ) + + eps, chi = calculate_strain_profile(sec, 0, m_cracking_pos) + draw_section_response( + sec, + eps, + chi, + title=f'M cracking pos {round(m_cracking_pos/1e6,2)} mkN', + ) + + eps, chi = calculate_strain_profile(sec, 0, m_cracking_neg) + draw_section_response( + sec, + eps, + chi, + title=f'M cracking neg {round(m_cracking_neg/1e6,2)} mkN', + ) + + return m_cracking_pos, m_cracking_neg + + +def calculate_width_at_z(geometry: CompoundGeometry | GenericSection, z): + """Calculate the width of a section or a section part at a certain level of z. + + Args: + geometry: the CompoundGeometry (part) or the whole section (GenericSection) + z: z value to evaluate the width in mm + + Returns: + Returns the width at z value + """ + + def width_fibre(geometry, z): + # Create a horizontal line at the specified y-coordinate + min_y, min_z, max_y, max_z = geometry.bounds + horizontal_line = LineString([(min_y, z), (max_y, z)]) + + # Find the intersection of the horizontal line with the geometry + intersection = geometry.intersection(horizontal_line) + + # If the intersection is a MultiLineString, get the total length + if intersection.is_empty: + return 0 + elif intersection.geom_type == 'LineString': + return intersection.length + elif intersection.geom_type == 'MultiLineString': + return sum(segment.length for segment in intersection) + else: + return 0 + + width = 0 + if isinstance(geometry, Polygon): + width = width_fibre(geometry, z) + elif isinstance(geometry, CompoundGeometry): + for g in geometry.geometries: + width += width_fibre(g.polygon, z) + elif isinstance(geometry, GenericSection): + for g in geometry.geometry.geometries: + width += width_fibre(g.polygon, z) + + return width + + +def effective_depth(section: GenericSection, neg_bending: bool = False): + """Calculate the width of a section or a section part at a certain level of z. + + Args: + section (GenericSection). + neg_bending: If negative=True the function return 'd' for negative bending moments + z: z value to evaluate the width in mm + + Returns: + Returns the efective depth 'd' in mm + """ + if not section.geometry.reinforced_concrete: + raise TypeError('Not a reinforced concrete section') + min_y, max_y, min_z, max_z = section.geometry.calculate_extents() + res = section.section_calculator.calculate_bending_strength() + neutral_axe = -res.eps_a / res.chi_y + print('na. ', neutral_axe) + sum_a = 0 + sum_az = 0 + mid_z = 0.5 * (min_z + max_z) + + for pg in section.geometry.point_geometries: + pg_z = pg._point.y + if (neg_bending and pg_z < mid_z) or ( + not neg_bending and pg_z > mid_z + ): + sum_a += pg._area + sum_az += pg._area * pg_z + + if ( + sum_a == 0 + ): # To handle the case where sum_a is zero to avoid division by zero + return 0 + + if neg_bending: + return max_z - sum_az / sum_a + else: + return sum_az / sum_a diff --git a/error_N_M.png b/error_N_M.png new file mode 100644 index 00000000..6ec31a6e Binary files /dev/null and b/error_N_M.png differ diff --git a/structuralcodes/plots/__init__.py b/structuralcodes/plots/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/structuralcodes/plots/section_plots.py b/structuralcodes/plots/section_plots.py new file mode 100644 index 00000000..06eaf200 --- /dev/null +++ b/structuralcodes/plots/section_plots.py @@ -0,0 +1,377 @@ +import sys + +import matplotlib.pyplot as plt +import numpy as np +from shapely import Point + +sys.path.append('../') +from structuralcodes.materials.constitutive_laws import ( + ParabolaRectangle, + Sargin, +) + + +def get_stress_point(section, y, z, eps_a, chi_y, chi_z): + """Get the stress in a given point (y,z) inside the cross section. + + Args: + y,z : coordinates of point inside the c.s. + eps_a : strain at (0,0) + chi_y,chi_z : curvatures of the cross secction (1/mm) + + Returns: + stress in y,z + """ + pt = Point(y, z) + strain = [] + strain.append(eps_a + z * chi_y + y * chi_z) + for i, g in enumerate(section.geometry.point_geometries): + center = g._point + r = g._diameter / 2 + poly = center.buffer(r) + if poly.contains(pt) or poly.touches(pt): + return g.material.get_stress(strain)[0] + for i, g in enumerate(section.geometry.geometries): + poly = g.polygon + if poly.contains(pt) or poly.touches(pt): + return g.material.get_stress(strain)[0] + return None + + +def draw_section(section, title='', reduction_reinf=None): + """Draw the cross section in matplotlib. + + Args: + reduction_reinf : float to reduce the diameter in homogenised + section + """ + fig, ax = plt.subplots(figsize=(4, 4)) + for i, g in enumerate(section.geometry.geometries): + poly = g.polygon + x, y = poly.exterior.xy + ax.fill(x, y, color='lightcoral') + for i, g in enumerate(section.geometry.point_geometries): + center = g._point + if reduction_reinf is not None: + r = g._diameter / 2 / reduction_reinf + color = 'navy' + else: + r = g._diameter / 2 + color = 'darkred' + poly = center.buffer(r) + x, y = poly.exterior.xy + ax.fill(x, y, color=color) + ax.invert_yaxis() # Invert Y axis + + # same scale in X and Y + ax.set_aspect('equal', 'box') + ax.set_title(title if title else 'Section') + + # plt.grid(True) + plt.show() + + +def draw_section_response( + section, eps_a, chi_y, lim_Sneg=None, lim_Spos=None, title=None +): + """Draw the stres and strains of a cross section. + + Args: + eps_a : strain at (0,0) + chi_y : curvature in Y axis + lim_Sneg, lim_Spos: limits of stress in the plot + """ + fig, (ax, ax2) = plt.subplots(1, 2, figsize=(10, 5)) + if title is not None: + fig.suptitle(title) + ax.set_title('Section response - stress') + ax.set_xlabel('Stress [MPa]') + ax.set_ylabel('z') + ax.invert_yaxis() # Invert axis + if (lim_Sneg is not None) and (lim_Spos is not None): + ax.set_xlim(lim_Sneg, lim_Spos) + else: + lim_Sneg, lim_Spos = -1e11, 1e11 + ax.invert_xaxis() + + labels_stress = {} + labels_strain = {} + # Asumiendo que puedes obtener estos valores correctamente de tus objetos section y geometries + for i, g in enumerate(section.geometry.geometries): + poly = g.polygon + x_min, y_min, x_max, y_max = poly.bounds + z_range = np.linspace(y_min, y_max, 500) + stress = [ + get_stress_point( + section, 0.5 * (x_min + x_max), z, eps_a, chi_y, 0 + ) + for z in z_range + ] + ax.plot(stress, z_range, color='gray') + ax.fill_betweenx( + z_range, + 0, + stress, + where=(np.array(stress) >= 0), + facecolor='lightblue', + interpolate=True, + ) + ax.fill_betweenx( + z_range, + 0, + stress, + where=(np.array(stress) < 0), + facecolor='lightcoral', + interpolate=True, + ) + labels_stress[(stress[0], z_range[0])] = round(stress[0], 2) + labels_stress[(stress[-1], z_range[-1])] = round(stress[-1], 2) + + # strain + eps = [(eps_a + chi_y * z) * 1000 for z in z_range] + ax2.plot(eps, z_range, color='gray') + ax2.fill_betweenx( + z_range, + 0, + eps, + where=(np.array(stress) >= 0), + facecolor='lightblue', + interpolate=True, + ) + ax2.fill_betweenx( + z_range, + 0, + eps, + where=(np.array(stress) < 0), + facecolor='lightcoral', + interpolate=True, + ) + labels_strain[(eps[0], z_range[0])] = round(eps[0], 2) + labels_strain[(eps[-1], z_range[-1])] = round(eps[-1], 2) + + for i, g in enumerate(section.geometry.point_geometries): + center = g._point + r = g._diameter / 2 + poly = center.buffer(r) + x_min, y_min, x_max, y_max = poly.bounds + z_range = [y_min, y_max] + + # stress + stress = [ + get_stress_point( + section, 0.5 * (x_min + x_max), z, eps_a, chi_y, 0 + ) + for z in z_range + ] + ax.plot(stress, z_range, color='gray') + ax.fill_betweenx( + z_range, + 0, + stress, + where=(np.array(stress) >= 0), + facecolor='lightblue', + interpolate=True, + ) + ax.fill_betweenx( + z_range, + 0, + stress, + where=(np.array(stress) < 0), + facecolor='lightcoral', + interpolate=True, + ) + labels_stress[ + ( + min(max(stress[0], lim_Sneg), lim_Spos), + 0.5 * (z_range[0] + z_range[1]), + ) + ] = round(0.5 * (stress[0] + stress[1]), 1) + + # strain + eps = [(eps_a + chi_y * z) * 1000 for z in z_range] + ax2.plot(eps, z_range, color='gray') + ax2.fill_betweenx( + z_range, + 0, + eps, + where=(np.array(stress) >= 0), + facecolor='lightblue', + interpolate=True, + ) + ax2.fill_betweenx( + z_range, + 0, + eps, + where=(np.array(stress) < 0), + facecolor='lightcoral', + interpolate=True, + ) + + labels_strain[(eps[0], 0.5 * (z_range[0] + z_range[1]))] = round( + 0.5 * (eps[0] + eps[1]), 2 + ) + + for coords, text in labels_stress.items(): + ax.text( + *coords, text, fontsize=10, color='black', ha='left', va='center' + ) + for coords, text in labels_strain.items(): + ax2.text( + *coords, text, fontsize=10, color='black', ha='left', va='center' + ) + + # strains + ax2.set_title('Section response - strain') + ax2.set_xlabel('strain [mm/m]') + ax2.set_ylabel('z') + ax2.invert_yaxis() # Invert axis + ax2.invert_xaxis() + plt.show() + if abs(chi_y) > 0: + print(f'z neutral axis = {round(-eps_a/chi_y,2)}') + + +def draw_constitutive_law(ec_const, lim_strain_neg=None, lim_strain_pos=None): + if isinstance(ec_const, ParabolaRectangle): + strain_p = 0 + strain_n = ec_const._eps_u + elif isinstance(ec_const, Sargin): + strain_p = 0 + strain_n = ec_const._eps_cu1 + else: + strain_p, strain_n = ec_const.get_ultimate_strain() + strain = np.linspace(strain_n, strain_p, 100) + stress = ec_const.get_stress(strain) + + fig, ax = plt.subplots(figsize=(4, 4)) + if (lim_strain_neg is not None) and (lim_strain_pos is not None): + ax.set_xlim(lim_strain_neg, lim_strain_pos) + else: + lim_strain_neg, lim_strain_pos = -1e11, 1e11 + + ax.plot(strain * 1000, stress, color='green') + ax.invert_yaxis() + ax.invert_xaxis() + ax.set_title(f'stress-strain curve {ec_const.name}') + ax.set_xlabel('Strain [mm/m]') + ax.set_ylabel('Stress [MPa]') + ax.axhline(0, color='gray', linewidth=1) + ax.axvline(0, color='gray', linewidth=1) + + # ax.set_xticks([strain[0], strain[-1]]) + # ax.set_xticklabels([round(strain[0], 3), round(strain[-1], 3)]) + # ax.set_yticks([stress[0], stress[-1]]) + # ax.set_yticklabels([round(stress[0], 2), round(stress[-1], 2)]) + + x_legend = ( + np.linspace( + max(strain[0], lim_strain_neg), min(lim_strain_pos, strain[-1]), 5 + ) + * 1000 + ) + y_legend = np.linspace(stress[0], stress[-1], 5) + ax.set_xticks(x_legend) + ax.set_yticks(y_legend) + ax.set_xticklabels(np.around(x_legend, decimals=3)) + ax.set_yticklabels(np.around(y_legend, decimals=2)) + + plt.show() + + +def draw_My_Mz_diagram(res, n=0, figsize=(10, 10), nticks_x=10, nticks_y=10): + """Draw the My-Mz diagram of a cross section. + + Args: + res : MMInteractionDomain results + figsize : Size of the plot + nticks_x,nticks_y : number of ticks in the axes + """ + m_y = res.m_y / 1e6 + m_z = res.m_z / 1e6 + fig, ax = plt.subplots(figsize=figsize) + ax.plot(m_y, m_z, color='green') + ax.set_title(f'My-Mz N={n/1e3} kN') + ax.set_xlabel('My (mkN)') + ax.set_ylabel('Mz (mkN)') + ax.axhline(0, color='gray', linewidth=1) + ax.axvline(0, color='gray', linewidth=1) + + x_legend = np.linspace(np.min(m_y), np.max(m_y), nticks_x) + y_legend = np.linspace(np.min(m_z), np.max(m_z), nticks_y) + ax.set_xticks(x_legend) + ax.set_yticks(y_legend) + ax.set_xticklabels(np.around(x_legend, decimals=1)) + ax.set_yticklabels(np.around(y_legend, decimals=1)) + plt.show() + + +def draw_N_M_diagram(res, figsize=(10, 10), nticks_x=10, nticks_y=10): + """Draw the N-M diagram of a cross section. + + Args: + res : NMInteractionDomain results + figsize : Size of the plot + nticks_x,nticks_y : number of ticks in the axes + """ + n = res.n / 1e3 + m_y = res.m_y / 1e6 + m_z = res.m_z / 1e6 + fig, ax = plt.subplots(figsize=figsize) + ax.plot(n, m_y, color='red') + ax.set_title('N-My') + ax.set_xlabel('N (kN)') + ax.set_ylabel('My (mkN)') + ax.axhline(0, color='gray', linewidth=1) + ax.axvline(0, color='gray', linewidth=1) + x_legend = np.linspace(np.min(n), np.max(n), nticks_x) + y_legend = np.linspace(np.min(m_y), np.max(m_y), nticks_y) + ax.set_xticks(x_legend) + ax.set_yticks(y_legend) + ax.set_xticklabels(np.around(x_legend, decimals=1)) + ax.set_yticklabels(np.around(y_legend, decimals=1)) + plt.show() + + fig, ax = plt.subplots(figsize=figsize) + ax.plot(n, m_z, color='red') + ax.set_title('N-MZ') + ax.set_xlabel('N (kN)') + ax.set_ylabel('Mz (mkN)') + ax.axhline(0, color='gray', linewidth=1) + ax.axvline(0, color='gray', linewidth=1) + x_legend = np.linspace(np.min(n), np.max(n), nticks_x) + y_legend = np.linspace(np.min(m_z), np.max(m_z), nticks_y) + ax.set_xticks(x_legend) + ax.set_yticks(y_legend) + ax.set_xticklabels(np.around(x_legend, decimals=1)) + ax.set_yticklabels(np.around(y_legend, decimals=1)) + plt.show() + + +def draw_M_curvature_diagram(res, figsize=(10, 10), nticks_x=10, nticks_y=10): + """Draw the N-M diagram of a cross section. + + Args: + res : MomentCurvatureResults results + figsize : Size of the plot + nticks_x,nticks_y : number of ticks in the axes + """ + fig, ax = plt.subplots(figsize=figsize) + ax.set_title(f'M-X N={res.n/1e3} kN') + ax.set_xlabel('X (1/km)') + ax.set_ylabel('My (mkN)') + ax.axhline(0, color='gray', linewidth=1) + ax.axvline(0, color='gray', linewidth=1) + xmin, xmax, ymin, ymax = 0, 0, 0, 0 + ax.plot(res.chi_y * 1e6, res.m_y / 1e6, color='red') + xmin = min(xmin, np.min(res.chi_y * 1e6)) + ymin = min(ymin, np.min(res.m_y / 1e6)) + xmax = max(xmax, np.max(res.chi_y * 1e6)) + ymax = max(ymax, np.max(res.m_y / 1e6)) + x_legend = np.linspace(xmin, xmax, nticks_x) + y_legend = np.linspace(ymin, ymax, nticks_y) + ax.set_xticks(x_legend) + ax.set_yticks(y_legend) + ax.set_xticklabels(np.around(x_legend, decimals=1)) + ax.set_yticklabels(np.around(y_legend, decimals=1)) + # plt.legend() + plt.show() diff --git a/structuralcodes/sections/_generic.py b/structuralcodes/sections/_generic.py index 60d72f29..bf9d6174 100644 --- a/structuralcodes/sections/_generic.py +++ b/structuralcodes/sections/_generic.py @@ -411,7 +411,7 @@ def _prefind_range_curvature_equilibrium( existence of at least one zero in the function dn vs. curv in order to apply the bisection algorithm. """ - ITMAX = 20 + ITMAX = 50 sign = -1 if dn_a > 0 else 1 found = False it = 0 diff --git a/tests/test_core/test_generic_section_SLS.py b/tests/test_core/test_generic_section_SLS.py new file mode 100644 index 00000000..f7e0a636 --- /dev/null +++ b/tests/test_core/test_generic_section_SLS.py @@ -0,0 +1,203 @@ +import os +import sys + +import numpy as np +import pytest +from shapely import Polygon + +from structuralcodes.geometry import CompoundGeometry, SurfaceGeometry +from structuralcodes.materials.concrete import ConcreteEC2_2004 +from structuralcodes.materials.reinforcement import ReinforcementEC2_2004 +from structuralcodes.sections import GenericSection +from structuralcodes.sections._reinforcement import add_reinforcement_line + +current_dir = os.path.dirname(os.path.abspath(__file__)) +parent_dir = os.path.dirname(os.path.dirname(current_dir)) +sys.path.append(parent_dir) + +from SLS_section_response import ( + calculate_cracking_moment, + calculate_strain_profile, + calculate_strain_profile_batch, + calculate_width_at_z, + effective_depth, +) + + +def test_calculate_width_at_z(): + """Test width_at_z in Polygon, CompoundGeometry and GenericSection.""" + # Test with a simple polygon + polygon = Polygon([(0, 0), (4, 0), (4, 4), (0, 4)]) + assert calculate_width_at_z(polygon, 2) == 4 + + # Test with a polygon where z is outside the bounds + assert calculate_width_at_z(polygon, 5) == 0 + + # Test with a compound geometry + compound_geometry = CompoundGeometry( + [ + SurfaceGeometry( + Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), ConcreteEC2_2004(25) + ), + SurfaceGeometry( + Polygon([(3, 0), (5, 0), (5, 2), (3, 2)]), ConcreteEC2_2004(25) + ), + ] + ) + assert calculate_width_at_z(compound_geometry, 1) == 4 + + # Test with a generic section + generic_section = GenericSection(compound_geometry) + assert calculate_width_at_z(generic_section, 1) == 4 + + # Test with an empty polygon + empty_polygon = Polygon() + assert calculate_width_at_z(empty_polygon, 1) == 0 + + # Test with an reinforced concrete section + concrete = ConcreteEC2_2004(25) + reinforcemnet = ReinforcementEC2_2004( + fyk=500, + Es=200000, + density=7850, + ftk=500, + epsuk=0.07, + ) + poly = Polygon(((0, 0), (0, 500), (350, 500), (350, 0))) + geo = SurfaceGeometry(poly, concrete) + geo = add_reinforcement_line( + geo, (50, 50), (300, 50), 12, reinforcemnet, n=3 + ) + geo = add_reinforcement_line( + geo, (50, 450), (300, 450), 20, reinforcemnet, n=6 + ) + generic_section = GenericSection(geo) + assert calculate_width_at_z(generic_section, 1) == 350 + + +def test_effective_depth(): + """Test effective_depth 'd' in GenericSection.""" + concrete = ConcreteEC2_2004(25) + reinforcemnet = ReinforcementEC2_2004( + fyk=500, + Es=200000, + density=7850, + ftk=500, + epsuk=0.07, + ) + poly = Polygon(((0, 0), (0, 500), (350, 500), (350, 0))) + geo = SurfaceGeometry(poly, concrete) + geo = add_reinforcement_line( + geo, (50, 60), (300, 60), 12, reinforcemnet, n=3 + ) + geo = add_reinforcement_line( + geo, (50, 450), (300, 450), 20, reinforcemnet, n=6 + ) + section = GenericSection(geo) + + # Test for positive bending (neg_bending=False) + effective_d = effective_depth(section, neg_bending=False) + assert effective_d == 450, f'Expected 450, got {effective_d}' + + # Test for negative bending (neg_bending=True) + effective_d_neg = effective_depth(section, neg_bending=True) + assert effective_d_neg == 440, f'Expected 440, got {effective_d_neg}' + + +def test_calculate_cracking_moment(): + """Test cracking moments in reinforced concrete section.""" + concrete = ConcreteEC2_2004(25) + reinforcemnet = ReinforcementEC2_2004( + fyk=500, + Es=200000, + density=7850, + ftk=500, + epsuk=0.07, + ) + # Create section + poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500))) + geo = SurfaceGeometry(poly, concrete) + geo = add_reinforcement_line( + geo, (50, 50), (300, 50), 12, reinforcemnet, n=3 + ) + geo = add_reinforcement_line( + geo, (50, 450), (300, 450), 20, reinforcemnet, n=6 + ) + sec = GenericSection(geo) + mcr_pos, mcr_neg = calculate_cracking_moment(sec, plot=False) + mcr_pos = mcr_pos * 1e-6 + mcr_neg = mcr_neg * 1e-6 + assert mcr_pos == pytest.approx( + 46.286, abs=1 + ), f'Expected approximately 46.286, got {mcr_pos}' + assert mcr_neg == pytest.approx( + -41.889, abs=1 + ), f'Expected approximately -41.889, got {mcr_neg}' + + +def test_calculate_strain_profile(): + """Test strain profile given N and M.""" + concrete = ConcreteEC2_2004(25) + reinforcemnet = ReinforcementEC2_2004( + fyk=500, + Es=200000, + density=7850, + ftk=500, + epsuk=0.07, + ) + # Create section + poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500))) + geo = SurfaceGeometry(poly, concrete) + geo = add_reinforcement_line( + geo, (50, 50), (300, 50), 12, reinforcemnet, n=3 + ) + geo = add_reinforcement_line( + geo, (50, 450), (300, 450), 20, reinforcemnet, n=6 + ) + sec = GenericSection(geo) + eps, chi = calculate_strain_profile(sec, 0, 200 * 1e6) # m = 200 mkN + eps = eps * 1e3 + chi = chi * 1e6 + assert eps == pytest.approx( + -1.017, abs=1e-2 + ), f'Expected approximately -1.017 mm/m, got {eps}' + assert chi == pytest.approx( + 5.322, abs=1e-2 + ), f'Expected approximately 5.322 1/km, got {chi}' + + +def test_calculate_strain_profile_batch(): + """Test strain profile given N and np.array of M.""" + concrete = ConcreteEC2_2004(25) + reinforcemnet = ReinforcementEC2_2004( + fyk=500, + Es=200000, + density=7850, + ftk=500, + epsuk=0.07, + ) + # Create section + poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500))) + geo = SurfaceGeometry(poly, concrete) + geo = add_reinforcement_line( + geo, (50, 50), (300, 50), 12, reinforcemnet, n=3 + ) + geo = add_reinforcement_line( + geo, (50, 450), (300, 450), 20, reinforcemnet, n=6 + ) + sec = GenericSection(geo) + + my_ed = np.array([-50, 50, 100]) * 1e6 # mkN + n_ed = 0 * 1e3 # kN + eps_batch, chi_batch = calculate_strain_profile_batch(sec, n_ed, my_ed) + for i, eps1 in enumerate(eps_batch): + chi1 = chi_batch[i] + eps2, chi2 = calculate_strain_profile(sec, n_ed, my_ed[i]) + print(eps1, eps2) + print(chi1, chi2) + assert eps1 == pytest.approx( + eps2, rel=1e-2, abs=1e-8 + ), f'Expected approximately {eps2} , got {eps1}' + assert chi1 == pytest.approx( + chi2, rel=1e-2, abs=1e-8 + ), f'Expected approximately {chi2} , got {chi1}'